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Open Access Article
1 - Implementation of Random Forest Algorithm in Order to Use Big Data to Improve Real-Time Traffic Monitoring and Safety
Negin Fatholahzade Gholamreza Akbarizadeh Morteza Romoozi -
Open Access Article
2 - Investigation on land cover mapping using Sentinel-2A images in the Google Earth Engine Platform
Naser Ahmadi SaniLand cover map show the spatial distribution of different landscapes such as agricultue, natural resources, water and man-made area. It is a valuable tool to managing and reducing risk in challenging issues such as drought and its effects, food security, flood control, MoreLand cover map show the spatial distribution of different landscapes such as agricultue, natural resources, water and man-made area. It is a valuable tool to managing and reducing risk in challenging issues such as drought and its effects, food security, flood control, and urban planning. In order to overcome the limitations of field work in the mapping of land cover, the use of satellite images due to the wide, multispectral and update data seems to be suitable. In the study area, the spatially heterogeneous landscapes also makes it difficult to classify features. Therefore, the main purpose of the study is accurate and high resolution land cover mapping using Sentinel-2A images in the Google Earth Engine platform. In this regard, three classification algorithms including RF, SVM and CART were evaluated and compared. Various indices were prepared using ratioing and transformation methods. The accuracy of the classifications was evaluated in comparison with ground reference data. Individual bands evaluation showed that the best overall accuracy (49%) was obtained using the CVI index.The best overall accuracy and kappa coefficient of 86% and 0.82 were obtained by RF algorithm. Therefore, while pointing to the advantages of the GEE including easily accessible data and the ability to process and quickly compare of data, it can be claimed that Sentinel-2A images for land cover mapping in terms of cost, time and accuracy, have high efficiency and the map can be very useful for the management and decision making in different natural and man-made resources for the successful implementation of sustainable development. Manuscript profile -
Open Access Article
3 - Landslide susceptibility mapping using advanced machine learning algorithms (Case study: Sarovabad city, Kurdistan province)
Baharak Motamedvaziri Hemen Rastkhadiv Seyed Akbar Javadi Hasan AhmadiThe occurrence of landslides in mountainous areas may cause serious damage to road infrastructure, and may also lead to human deaths. Therefore, the purpose of this study is to landslide susceptibility mapping using advanced machine learning algorithms in Sarovabad city MoreThe occurrence of landslides in mountainous areas may cause serious damage to road infrastructure, and may also lead to human deaths. Therefore, the purpose of this study is to landslide susceptibility mapping using advanced machine learning algorithms in Sarovabad city. In this study, landslide susceptibility was determined using two advanced data mining algorithms including random forest (RF) and decision tree (DT). First, the point file of 166 landslides occurred in Sarovabad city was considered as the landslide inventory map. The landslide points are divided into training data (70%) and validation data (30%). A total of 16 parameters including slope, aspect, elevation, river proximity, road proximity, river density, fault proximity, fault density, road density, precipitation, land use, NDVI, lithology, earthquake, stream power index (SPI) and topographic wetness index (TWI) were used in order to landslide susceptibility mapping. Finally, the performance of the models was evaluated using the ROC curve. The results of the ROC showed that the decision tree and random forest models have AUC values of 0.942 and 0.951, respectively. Therefore, the random forest model has the highest AUC value compared to the decision tree and was the best model for predicting the risk of landslides in the future in the study area. Landslide potential maps are efficient tools; so that they can be used for environmental management, land use planning and infrastructure development. Manuscript profile -
Open Access Article
4 - Tree Species Identification using RGB Time Series and Multispectral Images Obtained from UAV
Mojdeh Miraki هرمز سهرابیDetailed information on forest combination is required for many environmental, monitoring, and forest protection purposes. The link between ecology and remote sensing provides valuable information for the study of forest trees to facilitate the study of ecosystem perfor MoreDetailed information on forest combination is required for many environmental, monitoring, and forest protection purposes. The link between ecology and remote sensing provides valuable information for the study of forest trees to facilitate the study of ecosystem performance and to measure the spatial distribution of vegetation. In recent years, the use of modern remote sensing methods and techniques based on UAVs have been used for regular updating of forest inventory. In this research, different data sources including multi-spectral and RGB images with very high spatial resolution, were used for tree species recognition in plain forests of Noor City located in Mazandaran province. Also, taking images was performed in the growing season to prepare a time series of UAV-RGB images for investigating the effect of tree crown phonological changes on classification accuracy. Following orthomosaic generation, RGB (NGB, NRB) and multi-spectral (NDVI, CIgreen) indices were calculated and the random forest classification method was used for forest species classification. Based on single-time images, late April images provided the highest overall accuracy (75%). However, the results of the time series obtained from RGB images showed an increase in accuracy of up to 86%. Species identification based on multispectral images obtained from the Sequoia sensor also provided 85% accuracy. The results showed that the single-time image at the appropriate time using a UAV-RGB, compared to taking a time series and using a UAV equipped with multispectral sensors, has acceptable and less expensive results for tree recognition in the study area. Manuscript profile -
Open Access Article
5 - Evaluating the potential of groundwater resources using a combination of data mining methods:(Case study: Hormozgan province, Sarkhon plain)
fateme riahi hasan vagharfard peyman daneshkar araste hamid kardan moghadamThe porpose of this study is the ground water potential mapping.using four methods, stochastic forest, GLM, Domain, and GAM. in addition, four methods for combining these methods for potential mapping were also evaluated. for this purpose, eleven criteria include the sl MoreThe porpose of this study is the ground water potential mapping.using four methods, stochastic forest, GLM, Domain, and GAM. in addition, four methods for combining these methods for potential mapping were also evaluated. for this purpose, eleven criteria include the slope , profile curvature ,(Topographic Curvature), total curvature, index (spi), index (spi), index (twi), land use, soil and demographic elevation model were used according to the experience of experts and researchers. in order to validate the 76 of wells with high discharge were, it has been used for simulation (70 %) and validation (30%) before modeling the linear test on the criteria, there is no linear relationship between variables. according to, the results of the evaluation using the ROC curve showed that all four used methods have excellent accuracy and AUC over 90%. then, the results of four methods were combined with mean averaging method. The final potential showed that 32.89% of the lands have good potential for exploiting groundwater resources.The results of the importance factors also showed that the slope, height, and power index were the most important factors. The results of this research can serve as information bases for planners and local authorities to evaluate, plan, manage, sustainably use and synthesize groundwater resources in the future. Manuscript profile -
Open Access Article
6 - Evaluation of the efficiency of artificial intelligence and bivariate statistical models in determining landslide prone areas in West Azerbaijan
Azad Aram Mohammad Reza Dalalian Siamak Saedi Omid Rafieian Samad DarbandiBackground and Aims: Landslide is one of the natural hazards that lead to a lot of human and financial losses. Researchers on the subject of landslide susceptibility are investigating the possibility of landslides with respect to topographic and geo-environmental condit MoreBackground and Aims: Landslide is one of the natural hazards that lead to a lot of human and financial losses. Researchers on the subject of landslide susceptibility are investigating the possibility of landslides with respect to topographic and geo-environmental conditions, and the obtained information is critical in landslide risk management Preparation of landslide sensitive points is an essential tool for assessing landslide risk and is very useful in better planning and management of these areas. In this research, models based on artificial intelligence and two statistical variables in determining landslide sensitive points in West Azerbaijan province have been studied and compared.Methods: Methods based on artificial intelligence and two statistical variables were used to prepare landslide-sensitive points in the province of West Azerbaijan, which is located in northwestern Iran. This study was conducted in four stages. The first stage: the study of landslides in the studied region based on the database of the Forests, Rangelands and Watershed Organization of Iran (FRWO) and the identification of 110 landslides through field surveys, interpretation of aerial photographs and Google Earth satellite images, the second stage: data collecting and creating a spatial databases of effective factors, the third stage: applying the Frequency Ratio (FR), Shannon Entropy (SE), Bagging (BA), Random Forest (RF) and hybrid model (RF-BA) and stage four: methods validating using the system performance curve (ROC). Based on field surveys and similar studies, 12 factors affecting landslide occurrence including altitude, slope angle, slope direction, distance from fault, distance from river, distance from road, drainage density, road density, rainfall, soil, land use and lithology were identified. In the field survey, 110 landslides were identified in West Azerbaijan. 70 percent of the data were randomly selected and used for modeling and 30 percent of the data were used for validation.Results: In terms of geographical directions, the southern direction with a weight of 1.49 had the greatest impact on the occurrence of landslides in the province. The least weight was related to flat areas where no landslide occurred. The results of slope factor showed that the middle slopes had the greatest effect on the occurrence of landslides, so that in low slopes due to low gravity, less landslides occur and too much slopes were related to mountainous areas that were covered with rocks and there was very thin soil that is not suitable for landslide. The study of land use factor showed that 48 percent of landslides occured in agricultural areas. The results showed that most of the landslides occurred near rivers and faults. Also, in some areas, the closest distances to the road had the greatest risk of landslideConclusion: The results of this study showed that the artificial intelligence models (RF and the combined model RF-BA) had the higher efficiency than the statistical models (FR and SE). The accuracy of the combined models was higher than the single models. The ROC curve results showed the accuracy of 0.92, 0.91, 0.89 and 0.88 with RF-BA, RF, FR and SE models, respectively. Manuscript profile -
Open Access Article
7 - Modeling Soil Organic Matter Distribution Using Remote Sensing and Random Forest Model and Kriging in Lenjan County
Fatemeh Shiranitabar Mozhgan Ahmadi NadoushanBackground and Aim: Soil is one of the most important natural resources that provides more than 97% of human food needs. Soil organic matter (SOM) is an important soil quality factor that greatly affects soil’s physical, chemical, and biological properties. Modeli MoreBackground and Aim: Soil is one of the most important natural resources that provides more than 97% of human food needs. Soil organic matter (SOM) is an important soil quality factor that greatly affects soil’s physical, chemical, and biological properties. Modeling and mapping of soil properties are critical in many environmental, climatic, ecological, and hydrological applications. The main objective of this study is to model the distribution of soil organic matter and organic carbon using satellite images and random forest and kriging models in Lenjan County.Method: In this study, digital maps of four main soil parameters including soil organic carbon, soil organic matter, electrical conductivity, and pH are prepared using random forest and Kriging methods in Lenjan County. Based on homogeneous land units, a total of 110 points in the study area are determined, and in these points, samples are taken from a depth of 0 to 30 cm of soil surface. Sampling is done in July 2021 and Sentinel-2 satellite images are acquired from the same month because better information is available this month due to fewer clouds and increased direct reflection from the soil surface. In addition, 16 environmental variables affecting the distribution of soil parameters are used. Various auxiliary variables such as NDVI, NDWI, DEM, and Slope are used for prediction, which are all directly or indirectly extracted from satellite images.Results: The maps obtained by the random forest method showed more accuracy than the kriging method. The zoning map prepared using the random forest method displays much more details than the map prepared by kriging method. The output of the random forest model with the combination of different auxiliary variables showed values equal to 0.312, 0.54, 0.73 and 0.16 of the modeling error for soil organic carbon, organic matter, electrical conductivity and pH, respectively. In the study area, the maximum values of soil organic carbon and organic matter were observed in urban areas and the highest values of electrical conductivity and pH were observed in agricultural lands. The most important variables affecting the spatial distribution of organic carbon and soil organic matter are clay, slope and silt. While in modeling electrical conductivity, silt BI and Aspect and in modeling pH, MNDWI, NDWI and DEM variables are recorded as more important than other variables.Conclusion: In general, this study demonstrates that land use regression models based on random forest method can help mapping soil parameters faster and more efficiently. There is a strong need for efficient and accurate methods, including land use regression, for continuous monitoring of changes in soil quality in different landscapes. Land use regression contributes developing advanced maps of soil quality parameters using cost-effective and accessible spatial information. Manuscript profile -
Open Access Article
8 - Evaluation of Time Series Analysis Based on Wavelet Function on River Flow Simulation
Vahed Eslamitabar Ahmad Sharafati Farshad Ahmadi Vahid RezaverdinejadIntroduction: Using the values analyzed by the wavelet function can increase the accuracy of the simulations. Considering the climatic changes and the increase of extreme values in recent years, in this study, we made an effort that the effect of signal processing under MoreIntroduction: Using the values analyzed by the wavelet function can increase the accuracy of the simulations. Considering the climatic changes and the increase of extreme values in recent years, in this study, we made an effort that the effect of signal processing under the name of wavelet transformation in improving the performance of random forest model in simulating monthly river flow in Siminehrood and Mahabadchai sub-basins in the south of Lake Urmia has been discussed and investigated in the period of 1971-2019. Materials and Methods: In this study, the accuracy of the random forest model has been investigated in two steps of training and testing. At first, the random forest model was evaluated in two phases of training and testing in rainfall-runoff simulation in the south of Lake Urmia basin. Nash-Sutcliffe statistics and root mean square error were used to evaluate the performance and error rate of the studied models, respectively. In the next step, after investigating the performance of the random forest model, the time series of rainfall and river flow in the studied basins were analyzed using the wavelet function. In this regard, two analysis levels (level 1 and 2) and two Haar and Daubechies wavelet functions were used. Finally, using the random forest model, rainfall-runoff simulation based on the wavelet theory was done under the name of W-RF model. Results and Dissection: At First, the random forest model was investigated in two phases of training and testing, and the simulation results of the river flow values showed that the simulated values were within the 95% confidence interval, and the error rate of the river flow simulation using the RMSE statistic is 3.22 and 8.91 cubic meters per second in the test phase for Mahabadchai and Siminehrood sub-basins, respectively. In order to investigate the effect of time series analysis on the performance of the RF model, wavelet theory and Haar and Daubechies 4 wavelet functions were used in decomposition levels 1 and 2. By estimating the accuracy and performance of the hybrid W-RF model in 4 input patterns, the best pattern was selected based on the RMSE and NSE model evaluation criteria. The research results showed that for the Haar wavelet function in level 1 decomposition has better performance and error rate than level 2 type in both sub-basins. In this study, the Daubechies wavelet at level 1 in the test phase has provided the best performance and the lowest error rate in the simulation of the river flow values in the studied sub-basins and has been able to reduce the error rate in the two sub-basins of Mahabadchai and Siminehrood respectively by about 89 and 80 percent compared to the random forest model. Conclusion: Finally, by comparing the RF and W-RF models, the simulation results of river flow in the two studied sub-basins showed that the integrated W-RF model was able to reduce the error rate in the two sub-basins of Mahabadchai and Siminehrood to reduce by 89 and 80% respectively. Considering the increase in simulation complexity with the involvement of wavelet theory, the error recovery rate and model performance are acceptable. The integrated W-RF model in this study provides reliable results for the simulation of river flow data in order to support decision-making and risk analysis in the exploitation of downstream reservoirs and the management of water resources in sub-basins. The obtained results can be used in the design of water resources systems. Manuscript profile -
Open Access Article
9 - Optimal short-term prediction of initial supply yields using bat and random forest algorithms
hosein rostamkhani behroz khodarahmi Azita JahanshadThe purpose of this study is to predict short-term stock returns in initial public offerings using random bat and forest algorithms. In this study, companies that were listed on the OTC market of Iran for the first time during the period 1394 to 1399 were selected as a MoreThe purpose of this study is to predict short-term stock returns in initial public offerings using random bat and forest algorithms. In this study, companies that were listed on the OTC market of Iran for the first time during the period 1394 to 1399 were selected as a statistical sample. MATLAB software was used to analyze the data. Two scenarios were proposed to test the hypotheses. The first scenario was considered as annual and the second scenario as 6 years. Financial data with 11 factors: short-term market return, short-term return on new stock, market trends, company age, company size, annual sales, return on assets, return on equity, initial public offering price, operating profit, Cash flow from operations as influential factors and excess return of the offered share relative to the influential operating market entered the algorithms as input assumptions to predict the optimal amount. The results obtained from the bat algorithm indicate that the bat algorithm was able to provide better performance in predicting short-term stock returns in initial public offering in both scenarios and is not much different. While the results of accuracy in predicting the random forest algorithm in the second scenario compared to the first scenario has increased by about 12%. It can be concluded that the use of emerging bat and jungle algorithms in predicting short-term returns can help investors in predicting maximum returns and selecting the best stocks based on a precise and accurate pattern. Manuscript profile -
Open Access Article
10 - Predicting the factors affecting on the transfer (Migration) of value Firms with financial health in the Tehran Stock Exchange by using the nonlinear algorithm of random forest
Majid Rahmani Zadalh Fathi Mir Feiz FallahshamsThe purpose of this research is to investigate the factors affecting the transfer (migration) of value companies with financial health in the Tehran Stock Exchange and provide a model in this regard. In order to make better decisions and also to innovate, this research MoreThe purpose of this research is to investigate the factors affecting the transfer (migration) of value companies with financial health in the Tehran Stock Exchange and provide a model in this regard. In order to make better decisions and also to innovate, this research was conducted only on companies that have financial health based on the Black, Scholes and Merton model. The statistical population of this study is all companies listed on the Tehran Stock Exchange between 2012-2019. So we have used all the eligible companies. Therefore, we have used all the eligible companies; in general, 701 healthy companies have been used in this research. First portfolios were formed in the years 2012 to 2019 and then value shares were identified between different years.The present study provides a model for predicting stock value transfer using a nonlinear algorithm of random forests. Based on the results, it can be acknowledged that using historical information; we can suggest factors for modeling the value transfer. Manuscript profile -
Open Access Article
11 - Analyzing the Efficiency of the Deep Neural Network in Detecting Urban Changes Using Bi-temporal Landsat-8 Images.
sahand tahermanesh Behnam Asghari Beirami Mehdi mokhtarzadeSatellite remote sensing images are widely used to monitor the earth's surface phenomena changes at various periods. For accurate change detection, spatial features can be used as the complement information of spectral features. Hand-craft spatial features such as the c MoreSatellite remote sensing images are widely used to monitor the earth's surface phenomena changes at various periods. For accurate change detection, spatial features can be used as the complement information of spectral features. Hand-craft spatial features such as the co-occurrence matrix features are inefficient in detecting the changes due to the complex structure of satellite images. In the present study, a deep learning-based model is proposed as the alternative to address the problems of classical change detection methods. deep neural networks are mainly developed for images and hierarchically extracting spatial-spectral features. In this study, Landsat-8 images between 2013 and 2021 were used to evaluate the changes in Sahand city using the proposed deep network. Pre- and post-classified Landsat-8 images are produced using a deep neural network in the first stage. In the second stage, for producing the change maps, the post-classification approach is used in that change maps are produced based deference of classified images. Finally, the majority voting technique eliminates the noises in change maps. The proposed method results are compared with those obtained by two classical machine learning methods, random forest, and artificial neural networks. According to the change detection results, the proposed deep learning network improves detection accuracy by 13.88% and 12.80% compared with artificial neural networks and random forests. Compared to the random forest and artificial neural networks, the proposed network has improved the overall accuracy of the from-to-change maps by 57.81% and 65.7%, respectively. Final results demonstrate that although Random forest and artificial neural networks have been able to identify the location of changes, they perform poorly in detecting the from-to changes Manuscript profile -
Open Access Article
12 - Land subsidence susceptibility modeling using random forest approach (Case study: Tasuj plane catchment)
Davoud Mokhtari Hamid Ebrahimy Saeed SalmaniLand subsidence occurrence in the Tasuj plane might become more frequent and hazardous in the near future due to its relationship with the water crisis and drought periods. In order to mitigate the damage caused by land subsidence, it is necessary to determine the susce MoreLand subsidence occurrence in the Tasuj plane might become more frequent and hazardous in the near future due to its relationship with the water crisis and drought periods. In order to mitigate the damage caused by land subsidence, it is necessary to determine the susceptible or prone areas. The purpose of this study is to produce land subsidence susceptibility map based on the random forest approach to land subsidence occurrence data and eleven environmental variables that have significant influence on land subsidence occurrences (altitude, slope, aspect, distance to drainage line, drainage density, distance from the fault, topographic wetness index, land cover, lithology, groundwater level and decline in groundwater level) were used as inputs of the random forest model. The random forest approach was applied to produce the land subsidence susceptibility map. The performance of the model was assessed using the receiver operating characteristics (ROC) curve and the area under the curve (AUC). The model results indicate the accuracy of 0.86. Based on the result of the mean decrease accuracy method, the most important conditioning factors were groundwater level, distance from the fault, and a decline in groundwater level, respectively. According to the result, about 18% and 11% of the study area was located within high to very high susceptibility classes. The result of this study can be used by stakeholders and local authorities to mitigate related hazards of land subsidence occurrences in the study area. Manuscript profile -
Open Access Article
13 - Studying changes in heat islands and land uses of the Minab city using the random forest classification approach and spatial autocorrelation analysis
Mohamad Kazemi Ali Reza Nafarzadegan Fariborz MohammadiThe purpose of this study was to assess the impact of the land use changes in the Minab city on the variations in the urban heat islands (UHI), using the random forest classification method and spatial statistics on TM and OLI Landsat images in 1988, 1998, 2008 and 2018 MoreThe purpose of this study was to assess the impact of the land use changes in the Minab city on the variations in the urban heat islands (UHI), using the random forest classification method and spatial statistics on TM and OLI Landsat images in 1988, 1998, 2008 and 2018. First, land surface temperature (LST) was calculated using the split-window and the single-channel algorithms. Land use map was generated using supervised classification random forest method and its performance was assessed by two criteria of overall accuracy and kappa coefficient. In order to survey spatial autocorrelation and clustering, pattern of hot spots, the two statistics of Anselin Local Moran's I and Getis-Ord GI were applied. In 1988, land uses of vegetated, barren, and urban built-up lands were occupied 30.1, 32.53 and 37.37 percent of the city area, respectively; in 2018, the areas of these land uses were 16.36, 9.56 and 74.08 percents, respectively. A threefold and twice-fold decrease in the area was observed for barren and vegetated lands, respectively; while the area of urban built-up lands had more than doubled. The calculated values for urban-heat-island ratio index (URI) in 1988, 1998, 2008, and 2018 were 0.45, 0.34, 0.11, and 0.22, respectively. The outcomes of two considered spatial statistics indicated the clustering, pattern for UHI of the Minab city. In addition, there was a good agreement between the results of Getis-Ord GI statistic (hotspots spatial analysis) and the Local Moran's I statistic (spatial autocorrelation) on the spatial pattern of heat and cool clusters. Manuscript profile -
Open Access Article
14 - Creating a graphical user interface (GUI) to automatically calculate the land surface temperature and use the results in evaluating temperature changes in land uses in Ardabil city
Hossein Fekrat Sayyad Asghari Saraskanrood Seyed Kazem AlavipanahBackground and Objective Land surface temperature is a vital indicator for studying environmental changes, hydrological conditions and the energy balance of the earth, which can also be used to monitor the temperature changes of cities. The lack of meteorological statio MoreBackground and Objective Land surface temperature is a vital indicator for studying environmental changes, hydrological conditions and the energy balance of the earth, which can also be used to monitor the temperature changes of cities. The lack of meteorological stations in most parts of the country, including the study area, has created information limitations in the field of surface temperature data. There are also a large number of non-remote sensing users who need LST maps, and most of them are not familiar enough with LST computing software and inevitably have to spend a lot of time mapping to prepare their maps. This process can be time-consuming even for remote sensing professionals if the number of images is high. The use of valid data for validation that has the least time difference with the satellite passes time is very important in estimating the accuracy of the results. By reviewing internal research similar to the one under study, most internal studies used only meteorological station data to validate the results, the data recording time at these stations is different from the satellite passes time. In this study, due to the large area of the study area and the insufficient number of meteorological stations, in addition to the surface temperature data measured in synoptic stations, the land surface temperature in two ground stations was recorded simultaneously with the satellite. Creating a graphical user interface (GUI) to automatically calculate the surface temperature of Ardabil city with two single-channel and RTE algorithms and use the results to evaluate the temperature changes of land usesMaterials and Methods In this study, in order to automatically calculate the land surface temperature of Ardabil city from three types of data: Landsat 5 and 8 satellite images, land surface temperature data recorded at two meteorological stations in the study area and also due to an insufficient number of stations Meteorological data land surface temperature data measured with digital thermometers are also used as the satellite passes. After preparing thermal and multispectral images, first MODTRAN web computing software was used to model the atmospheric transferability and atmospheric coefficients were extracted. Then, to create graphical user interfaces and automatic calculation of LST, land surface temperature with two algorithms single-channel and RTE method with Landsat 5 and Landsat 8 satellite images for two dates: 31/07/2000 and 21/08/2019 in MATLAB software were coded and using these codes, graphical user interfaces were created for each algorithm and finally, an automatic land surface temperature calculator application was produced. Also, the land use map of Ardabil city for both mentioned dates was classified and extracted using a random forest algorithm in the Google Earth engine system environment with 7 classes. This algorithm has a much better performance compared to traditional methods such as maximum likelihood due to its hierarchical structure in selecting each pixel to the appropriate class. To validate surface temperature maps from two types of surface temperature data recorded in two meteorological stations and surface temperature recorded by a digital thermometer that simultaneously passes the satellite in two points of the homogeneous non-urban environment with agricultural use (alfalfa) and Bayer that product It was harvested, used. To evaluate the accuracy of land use maps, using Google Earth, which has a better spatial resolution than the image used, 248 ground control points were obtained from pure pixels of different land uses and used in the validation process. Also, statistical parameters such as error matrix, overall accuracy and kappa coefficient were applied to the output of both land use maps.Results and Discussion Using the codes written in MATLAB software, graphical user interfaces (GUI) were created and then the automatic LST calculator application was produced. The output of the application was surface temperature maps with single channel algorithms and radiation transfer equation (RTE) for 31/07/2000 using thermal image (band 6) of Landsat 5 satellite TM and 21/08 / 2019 was created by the 10 TIRS sensor band of Landsat 8 satellite. After comparing the output maps with the meteorological station and ground station data, the results showed that the single-channel method had the lowest temperature deviations compared to the stations in both years. After preparing LST maps and selecting the optimal algorithm (single channel), land use maps of Ardabil city were prepared using a random forest algorithm in the GEE platform. Statistical evaluations of the classification results showed that for 2000, the highest pixel interference was related to the middle and poor rangeland class, which has a 16-pixel displacement with residential and rainfed agricultural classes. Due to the improved spatial resolution of the Landsat 8 satellite compared to the Landsat 5, followed by better class separation, this pixel displacement in the 2019 user map shows a smaller value. The most common error was related to the aquaculture class, which had a displacement of 10 pixels with rich rangeland and rainfed agriculture classes. Finally, using the LST map and land use map, the temperature changes of the land uses over a period of 19 years were evaluated. By entering the input images and atmospheric parameters in the application, the land surface temperature was calculated with two one-channel algorithms and the RTE method. Evaluation of output maps with meteorological and terrestrial data showed that the single-channel algorithm with a difference of +2.5 and -2 with stations 1 and 2 for the year 2000 and with a temperature difference of +1.3, +0.9, -1 and -0.9 with stations 1, 2, 3 and 4 in 2019, respectively, had higher accuracy than the RTE method. Also, the results of validation of land use maps showed an overall accuracy of 0.95 and a kappa coefficient of 0.94 for 2000 and overall accuracy of 0.96 and a kappa coefficient of 0.95 for 2019.Conclusion Assessing the relationship between land surface temperature and land use maps showed that despite the significant physical growth of the urban sector over a period of 19 years, except for residential areas, all land uses in 2019 compared to 2000 with an increase in average surface temperature. It seems that factors such as the expansion of agricultural lands with irrigated cultivation around the urban area up to a radius of 10 km and the entanglement of these farms with the urban sector have a great impact on the temperature adjustment of the urban sector. In 2000, these lands were mainly under cultivation of rain-fed crops, and by solving the water problem (digging deep wells and water transfer projects), they became orchards and irrigated farms such as potatoes. Due to the high water requirement, these products also have high greenery, and this factor has increased the rate of evapotranspiration, followed by cooling of the cultivation area and the urban sector. Among other classes, in both years of water use, the lowest and the use of barren lands had the highest average surface temperature. The generated application can be run on any operating system that supports the exe format, and the user by specifying atmospheric parameters can automatically estimate the LST. This application can also be used in various sectors such as agricultural systems, and climate and water resources management. Manuscript profile -
Open Access Article
15 - Comparison of the effectiveness of machine learning methods in modeling fire-prone areas (Ilam Province, Darehshahr City)
maryam mohammadian Maryam Morovati Reza OmidipourFire is one of the most important natural hazards that has a great impact on the structure and dynamics of natural ecosystems. Due to Iran's location in the arid and semi-arid belt of the world, a large number of human-made and natural fires occur in different regions o MoreFire is one of the most important natural hazards that has a great impact on the structure and dynamics of natural ecosystems. Due to Iran's location in the arid and semi-arid belt of the world, a large number of human-made and natural fires occur in different regions of the country every year. Therefore, determining sensitive areas to fire occurrence plays an important role in fire management in natural resources. To do so, the current study aims to identify fire-prone areas in Dere Shahr city in Ilam province using two machine learning of random forest (RF) and support vector machine (SVM) and 2024 fire occurrence points. Environmental factors were prepared in categories including topographical factors (altitude, slope direction, slope anlgle), climatic factors (rainfall, relative humidity, wind, temperature), biological factors (vegetation and soil moisture) and man-made factors (distance from residential areas, distance from road, distance from agricultural land, distance from river). The model’s accuracy was evaluated using the area under the curve (AUC) in the ROC curve and cross-validation statistics. Examining the AUC index showed that both models had good accuracy, although the RF model (AUC = 0.97) had higher accuracy than the support vector machine model (AUC = 0.86). According to the results of RF model, about 60% are in the low-risk class and about 20% are in the high fire risk class. Investigating the contribution of the factors affecting the occurrence of fire showed that man-made factors (distance from residential areas) and climatic factors (temperature) played a more important role in areas with a history of fire. Therefore, increasing public culture and reducing dangerous behaviors in nature can reduce the occurrence of fire in this area and contribute greatly to the protection of the environment and preservation of natural resources. Manuscript profile -
Open Access Article
16 - Presenting the smart pattern of credit risk of the real banks’ customers using machine learning algorithm.
Hojjat Tajik Ghodratollah Talebnia Hamid Reza Vakili Fard Faegh Ahmadi -
Open Access Article
17 - Forecasting Stock Trend by Data Mining Algorithm
Sadegh Ehteshami Mohsen Hamidian Zohreh Hajiha Serveh Shokrollahi -
Open Access Article
18 - Landslide susceptibility modelling using integrated application of computational intelligence in Ahar County, Iran
Solmaz Abdollahizad Mohammad Ali Balafar Bakhtiar Feizizadeh Amin Babazadeh Sangar Karim Samadzamini -
Open Access Article
19 - Short-Term Tuberculosis Incidence Rate Prediction for Europe using Machine learning Algorithms
Jamilu Yahaya Maipan-uku Nadire Cavus Boran Sekeroglu -
Open Access Article
20 - Prioritization of factors affecting the occurrence of slope movements and preparation of a zoning map of the risk of its occurrence using a new random forest algorithm (Case study: part of the catchment area of Latian Dam)
Leila EbrahimiThe first and most important step in assessing the risk of sloping movements is to prepare risk maps for the occurrence of sloping movements. These maps are a final product that can be useful for land use planning. The purpose of this study is to prioritize the factors MoreThe first and most important step in assessing the risk of sloping movements is to prepare risk maps for the occurrence of sloping movements. These maps are a final product that can be useful for land use planning. The purpose of this study is to prioritize the factors affecting the occurrence of slope movements and zoning the risk of its occurrence using a new random forest algorithm in the topographic map of 1: 50,000 Lashkark in the northern part of Latian Dam. According to the hydrological, topographic, geological, geomorphological and climatic characteristics of the region, the most important factors influencing the occurrence of slope movements are 9 indices of distance from road, fault, river, lithology, rainfall, altitude, slope, slope direction and land use as the most important. Factors affecting the occurrence of this type of movement in the region were considered. The results show that the three factors of distance from the fault, road and slope are three important factors in the occurrence of amplitude movements in the study area, respectively. In order to evaluate the prepared model, the relative performance detection curve was used. Based on the results of the rock curve, the value of the area under the curve in educational points is equal to 0.826 and in evaluation points is equal to 0.839 percent. Figure 9 is a very good evaluation of the stochastic forest algorithm in zoning the risk of slope movements using this model. Manuscript profile -
Open Access Article
21 - Spatial analysis of landslide risk with emphasis on geomorphological factors using stochastic forest model (Case study: Larestan city in Fars province)
Mohammad Ibrahim AfifiDue to the ability of data mining techniques, their application in the field of earth sciences has been widely developed. The purpose of this study is to zoning landslide sensitivity using stochastic forest algorithm in Larestan city, Fars province. Random forests are a MoreDue to the ability of data mining techniques, their application in the field of earth sciences has been widely developed. The purpose of this study is to zoning landslide sensitivity using stochastic forest algorithm in Larestan city, Fars province. Random forests are a modern type of tree-base that includes a host of classification and regression trees. The random forest algorithm is based on a bunch of decision trees and is currently one of the best learning algorithms. For the present study, information layers of slope degree, slope direction, altitude, slope shape, distance from fault, distance from waterway, distance from road, rainfall, lithology and land use as factors affecting landslide occurrence were identified and its maps in software. ArcGIS10 / 2 digit and were prepared. Then, using a random forest algorithm, the relationship between the effective factors and the location of landslides and the weight of each of them were calculated in R statistical software and finally transferred to the GIS environment to prepare a landslide susceptibility map. The results of evaluating the accuracy of the zoning method using the relative yield detection curve and 30% of the slip points not used in the modeling process, indicate the excellent accuracy of the random forest model with the area below the curve being 98.8%. The executive recommendation is to reduce the risk of stabilization of unstable areas and to avoid these areas; And any planning in the future development of the physical elements of urban infrastructure should be done with regard to the possibility of landslides. Manuscript profile -
Open Access Article
22 - Presenting financial bankruptcy risk prediction model of stock and transborder companies using machine learning algorithms
Mohsen Aali Seyed Alireza Mirarab Baygi Nima FarajianBankruptcy or business failure can have a negative impact on both the company itself and the global economy. In this research, the financial bankruptcy risk prediction of stock and transborder companies has been done using machine learning algorithms, Where the ultimate MoreBankruptcy or business failure can have a negative impact on both the company itself and the global economy. In this research, the financial bankruptcy risk prediction of stock and transborder companies has been done using machine learning algorithms, Where the ultimate goal is to predict the financial bankruptcy risk of stock exchange and transborder companies. Collective learning is a field of machine learning in which instead of using a model to solve a problem, Use multiple models in combination to increase the output estimation power of the model. Each model is retrained using optimal features. As a result, the accuracy of predicting machine learning model by Stacking method, which is one of the strongest techniques of collective learning, To predict financial bankruptcy risk is higher than similar methods. Investors always want to prevent the deterioration of their capital by anticipating the possibility of a company's bankruptcy. Therefore, they are looking for ways to predict the bankruptcy of companies. Manuscript profile -
Open Access Article
23 - ADABOOST Ensemble Algorithms for Breast Cancer Classification
Moshood Hambali Yakub Saheed Tinuke Oladele Morufat Gbolagade -
Open Access Article
24 - Town trip forecasting based on data mining techniques
Mohammad Fili Majid Khedmati -
Open Access Article
25 - Comparing Different Modeling Techniques for Predicting Presence-absence of Some Dominant Plant Species in Mountain Rangelands, Mazandaran Province
Mansoureh Kargar Davod Akhzari Amir Saadatfar -
Open Access Article
26 - Predicting the Next State of Traffic by Data Mining Classification Techniques
S.Mehdi Hashemi Mehrdad Almasi Roozbeh Ebrazi Mohsen Jahanshahi -
Open Access Article
27 - Optimal stock selection using bat and random forest algorithm
hosein rostamkhani behroz khodarahmi azita jahanshadThe purpose of this study is to optimally select stocks using the bat and random forest algorithm. In this study, based on the analysis of 6 variables: stock price to earnings per share ratio, annual earnings growth rate, annual sales growth rate, return on assets, retu MoreThe purpose of this study is to optimally select stocks using the bat and random forest algorithm. In this study, based on the analysis of 6 variables: stock price to earnings per share ratio, annual earnings growth rate, annual sales growth rate, return on assets, return on equity and free float shares extracted from 181 companies listed on the Tehran Stock Exchange, It has been used during the period of 1394 to 1398. Six scenarios are considered to estimate the accuracy of the two algorithms, so that for scenarios 1 to 6, the algorithms are asked to participate 5, 10, 15, 20, 25 and 30, respectively. The results show that the nature of the random forest algorithm requires training and selection of features, which makes the algorithm faster and increases the convergence time. One of the main reasons for the higher accuracy of the random forest algorithm in scenarios 1 to 3 could be this. In scenarios 4 to 6, due to the increasing complexity of the problem, the accuracy of the random forest algorithm decreases, but due to the random nature of the bat algorithm, its accuracy does not differ much and it can maintain stability in its selection. Manuscript profile -
Open Access Article
28 - Predicting the Direction of Stock Market Prices Using Random Forest
elham gholamian sayyed mohammad reza davoodiStock market activists are the acquiring and using methods to predict future stock prices, increasing their capital gains. Therefore, it seems necessary that appropriate, correct, and scientific principles are used to determine the future price of the stock of investor MoreStock market activists are the acquiring and using methods to predict future stock prices, increasing their capital gains. Therefore, it seems necessary that appropriate, correct, and scientific principles are used to determine the future price of the stock of investor stock options.stock price prediction is an important part of investment, and in most cases it is the field of research for researchers, because it ultimately leads to the choice of appropriate investment. Different methods have now been developed to achieve this goal. Have been introduced that are often statistical methods and artificial intelligence. In this research, using a randomized approach approach that is among artificial intelligence classification methods, along with technical indicators that include: power index Relative Price, Stochastic, Equilibrium Balance, Williams R%, Daily Returns, and Mac.d Series Markets, are looking for stock price trends. This model is compared with logistic regression method and completely randomized method (dice throw). The results of the research on daily data of Tehran Stock Exchange Index from 1393 to 1395 indicate that the accuracy of the proposed method in estimating market trend is 64%, which is more than two methods of logistic regressionand completely randomized method of accuracy Has a higher rate. Manuscript profile -
Open Access Article
29 - Investigate the Operation of Random forest and Deep neural networks on Statistical Arbitrage Strategy
alireza Fazlzadeh Jafar Haghigha Faranak Pourkeivan vahid ahmadian In this research, the statistical analysis of random forest effects has been done. Also, to evaluate the performance of the random forest algorithm in the field of statistical arbitrage compared to other models presented in the previous research, the comparison of More In this research, the statistical analysis of random forest effects has been done. Also, to evaluate the performance of the random forest algorithm in the field of statistical arbitrage compared to other models presented in the previous research, the comparison of the results from the application of this algorithm with deep neural network algorithm has been done. The models are taught with stock price information and the output from this technique categorizes stocks according to the position of buying and selling. Using this strategy, profitable positions are identified in market shares for profit. The results showed that the model of random forest with less error classification than deep neural network model. Using this strategy, profitable positions are identified in market shares for profit. The results showed that the model of random forest with less error classification than deep neural network model. Manuscript profile -
Open Access Article
30 - Presenting the developed model of Benish model with emphasis on the special characteristics of the company using neural network, vector machine and random forest
Kiumars Pourgadimi Saeed jabbarzadeh Kangarloui Jamal Bahri SalesAbstractAs the business process becomes more complex, the risk of financial statements being distorted increases with each passing day. In this regard, researchers have been looking for models to detect fraud in financial statements. Benish (1997) used a combination of MoreAbstractAs the business process becomes more complex, the risk of financial statements being distorted increases with each passing day. In this regard, researchers have been looking for models to detect fraud in financial statements. Benish (1997) used a combination of financial ratios and accruals to predict profit manipulation methods. since auditors are presented as external oversight in the corporate governance structure of the company's performance, in this study the model is developed based on the qualitative characteristics of the auditor, which include the auditor's size, auditor tenure, reporting delay, Auditor Class and Auditor Change.The fitting of the stochastic vector machine, random forest and neural network has been used to fit the extended model. The results show that the coefficients obtained from the random forest model are 99% and more than the two neural network and vector model 94%. Manuscript profile -
Open Access Article
31 - Presenting the developed model of benish model with emphasis on audit quality fea-tures using neural network, vector machine and random forest
Kiumars Pourgadimi Jamal Bahri Sales Saeed Jabbarzadeh Kangarluei Akbar Zavar RezaeePurpose: As the business process becomes more complex, the risk of financial statements being distorted increases with each passing day. In this regard, researchers have been looking for models to detect fraud in financial statements. The purpose of this research is to MorePurpose: As the business process becomes more complex, the risk of financial statements being distorted increases with each passing day. In this regard, researchers have been looking for models to detect fraud in financial statements. The purpose of this research is to present an expanded model based on the quality characteristics of the auditor.Methodology: Benish (1997) used a combination of financial ratios and accruals to predict profit manipulation methods. Since auditors are presented as external oversight in the corporate governance structure of the company's performance, in this study the model is developed based on the qualitative char-acteristics of the auditor, which include the auditor's size, auditor tenure, reporting delay, Auditor Class and Auditor Change. The fitting of the vector machine, random forest and neural network has been used to fit the ex-tended model.Findings: The results show that the coefficients obtained from the random forest model are 98.4% and more than the two neural network and vector model 93%. Also, the extended model is more accurate than the base model. Audit characteristics are influential in predicting fraud in financial statements and should be considered by capital market participants.Originality / Value: Research findings can be effective in improving the prediction of fraud in financial statements and also draw users' attention to the combination of financial statement information and the characteristics of the auditor's report in fraud prediction. Manuscript profile -
Open Access Article
32 - Ability of Machine Learning Algorithms and Artificial Neural Networks in Predicting Accounting Profit Information Content Before Announcing
Hossein Alizadeh Majid Zanjirdar Gholam Ali HajiPurpose: The aim of this research is to investigate the capability of artificial neural networks and machine learning algorithms, including Support Vector Machine and Random Forest, in predicting the information content of accounting profits before its announcement in a MorePurpose: The aim of this research is to investigate the capability of artificial neural networks and machine learning algorithms, including Support Vector Machine and Random Forest, in predicting the information content of accounting profits before its announcement in accepted companies on the Tehran Stock Exchange during the period from 2015 to 2020.Methodology: Daily data required for the research were collected using Rahnaward-e-Novin software, and a systematic random sampling method was used to select 88 companies. MATLAB was used for modeling artificial neural networks and machine learning algorithms, and Python code was employed to calculate abnormal returns in neural networks and machine learning algorithms. The information content of profits was measured through the test of the relationship between profits and abnormal returns, based on the model by Porti et al. (2018). The input variables for artificial neural networks and machine learning algorithms are technical indicators. Accuracy, precision, recall, and F-score metrics were used for performance evaluation.Findings: The results of predicting with three models of artificial neural networks, Support Vector Machine, and Random Forest showed that Support Vector Machine and Random Forest had higher accuracy than artificial neural networks in predicting buy, sell, and hold strategies, and only Support Vector Machine had the ability to predict the information content of profits among the three models.Originality / Value: Designing a predictive model for stock price movements in the next trading day using artificial neural networks, Support Vector Machine, and Random Forest as the main innovation of the research. The research findings can increase the speed of information dissemination to the market and attract it, which will reduce the impact of informational asymmetry and information-based trading and ultimately enhance market efficiency. Manuscript profile
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