Support Vector Regression

Open Access Article

1 - Performance Evaluation of M5 Tree Model and Support Vector Regression Methods in Suspended Sediment Load Modeling
Mohammad Taghi Sattari علی رضازاده جودی Forugh Safdari فراز قهرمانیان

Sediment transport has always affected the river and civil structures and the lack of knowledge about its exact amount causes high damages. Therefore, it is very important to properly estimate the sediment load in rivers in terms of sediment, erosion and flood control. More

Sediment transport has always affected the river and civil structures and the lack of knowledge about its exact amount causes high damages. Therefore, it is very important to properly estimate the sediment load in rivers in terms of sediment, erosion and flood control. This study used two new data mining methods including M5 model tree and support vector regression comparing with the classic method of sediment rating curve to estimate the suspended sediment load in Aharchay River. To assess the performance of the used methods, three criteria including the correlation coefficient, root mean square error and mean absolute error were used. Analyzing the sensitivity of models to the input variables, it was found that the variable of flow discharge in the current month had the greatest effect on the amount of suspended sediment load. The results showed the high accuracy of new data mining methods in comparison with the sediment rating curve. Although, both considered data mining methods had more accuracy and less error compared to the conventional sediment rating curve, given the simple understandable linear relationships provided by the M5 model tree, this method is recommended for similar cases. Manuscript profile

Open Access Article

2 - Modelling monthly runoff by using data mining methods based on attribute selection algorithms
محمدتقی ستاری Ali Rezazadeh Joudi

Given the importance of catchment basin output flow for surface water management, precise understanding of the relationship between the amount of runoff and climatic parameters such as precipitation and temperature is important. therefore the identification of parameter More

Given the importance of catchment basin output flow for surface water management, precise understanding of the relationship between the amount of runoff and climatic parameters such as precipitation and temperature is important. therefore the identification of parameters are important in the modeling process.  In this paper, after homogeneity tests have been carried out for monthly precipitation, temperature, and runoff data in the Navroud Catchment Basin in Iran, two combinations of effective factors for runoff are considered according to Relief and Correlation algorithms. A new Relief Algorithm first identifies effective features within a set of data in an orderly manner especially when the amount of available data is low. The new method uses a data-related weight vector average and a threshold value. Applying support vector regression and the nearest neighbor method, monthly runoff was modeled based on the two proposed combinations. The results showed that support vector regression approach which utilizes a radial basis function kernel, yields higher accuracy and lower error than the nearest neighbor method for estimating runoff. The improvement is particularly noticeable for flooding situations. Manuscript profile

Open Access Article

3 - Application of hybrid ARIMA and support vector regression model for improvement of time series forecasting
Laleh Parviz Bahareh Saeedabdi

Accurate investigation related to the structure of time series plays an important role in increasing the accuracy of ARIMA forecasting. The aim of this research is to investigate the effect of modeling decomposition of linear and non linear parts of time series on ARIMA More

Accurate investigation related to the structure of time series plays an important role in increasing the accuracy of ARIMA forecasting. The aim of this research is to investigate the effect of modeling decomposition of linear and non linear parts of time series on ARIMA model results. The decomposition of wheat and maize yield time series (in Kermanshah and Esfahan provinces) in the linear part was related to ARIMA and in the non linear part was conducted with support vector regression (hybrid model). The kind of configuration of non linear part of hybrid model is more important for example in the maize time series of Kermanshah, the values of RMSE for configuration with residual was 1.52 and for time series configuration was 15.03. The decreasing of RMSE, MAE and UII for wheat time series of Esfahan with hybrid model was 45.94%, 52.29% and 46%, respectively which is indicative of hybrid model improvement. The value of GMER in all four time series was greater than one which indicates the overestimation of hybrid model. Comparison the average of each criteria with two models and crops in each province indicated the effect of climate on modeling process because the average of criteria in Esfahan province decreased rather to Kermanshah (RMSE decreasing= 24.72%, UII decreasing=12.24%). Therefore, decomposition of time series to linear and non linear parts of time series can increase the accuracy of ARIMA model results. Manuscript profile

Open Access Article

4 - Investigation of Soil Surface Moisture in Ardabil City Using Landsat 8 and Sentile 1 satellite Data
Sayyad Asghari Saraskanrod Fariba Esfandayari Darabad Elham Mollanouri Shiva Safary

10.30495/wsrcj.2022.20006

Background and Aim: Surface soil moisture is an important variable in nature's water cycle and can be affected by various factors, including temperature and soil characteristics. The use of ground sensors for measuring moisture can lead to spending time and expense and More

Background and Aim: Surface soil moisture is an important variable in nature's water cycle and can be affected by various factors, including temperature and soil characteristics. The use of ground sensors for measuring moisture can lead to spending time and expense and inappropriate distribution of samples on large scales. Therefore, Remote sensing observations can be an important tool in estimating soil moisture. The present study aims to use the TOTRAM model using Landsat 8 images and the SVR method using Sentile 1 images to estimate soil moisture.Methods: In the present study, two TOTRAM methods based on pixel distribution in LST- VI space and the SVR method were used to extract soil moisture using the SAR technique and Sentinel 1 data. To implement the TOTRAM method, Landsat 8 images related to 4/29/1398 and 5/30/1398 are downloaded and after extracting NDVI and LST maps, The correlation between the dependent variable of moisture and independent temperature variables and vegetation variables has been investigated using Geographically weighted regression (GWR). To implement the SVR method after acquiring Sentinel 1 images related to 31\/05\/1398 and 27\/04\/1398, Soil Moisture Data Product FLDAS and 500 meters product of Modis Satellite (MCD12q1) were called to classify land cover in the Google Earth Engine system, and maps related to soil moisture were extracted. After extracting the moisture maps the distribution of moisture using the local Moran index has been investigated. By defining this index, positive values for this index represent the cluster of distribution.Results: Examination of the soil moisture map obtained by the SVR method showed the concentration of moisture in areas with vegetation and water and the change in moisture status from July to August was visible. The humidity pattern has shown the reflection of the precipitation pattern so that maximum precipitation and humidity were observed in April and in summer both precipitation and humidity components decreased. Examination of the TOTRAM method and application of the GWR method has shown a complete correlation between NDVI LST and moisture. However, the correlation between LST and humidity with B (values) and standard error (SE) of 0.995 and zero corresponding to July and 0.981 and zero corresponding to August showed the highest correlation with vegetation variable with moisture dependence parameter, which this correlation In August, with increasing the coefficient of determination of R2 to 0.997 and a significant decrease of NDVI to the value of 0.415 in July, it has increased much more. Application of Moran local index with values less than 0.05 for p-value and positive values for z and near positive number 1 for Moran index showed the cluster distribution of moisture variable.Conclusion: The results of TOTRAM and SVR methods showed the dependence of soil moisture status on conditions and cluster moisture distribution. According to the correlation coefficients of geographical regression, there is a greater correlation between temperature and humidity variables, especially in August, due to the decrease in vegetation density. The results of the SVR algorithm maps showed that in areas with the presence of vegetation, especially dense vegetation, we see an increase and with increasing temperature, we see a decrease in humidity. Also, the coordination of moisture patterns of the SVR algorithm and precipitation showed a direct relationship between moisture and precipitation. Considering that the SVR method uses parameters such as radar scattering intensity and land cover classification, as well as the use of Sentinel 1 radar images by this algorithm, more accurate results can be expected from this algorithm.Keywords: LST, NDVI, Support vector regression, TOTRAMBackground and Aim: Surface soil moisture is an important variable in nature's water cycle and can be affected by various factors, including temperature and soil characteristics. The use of ground sensors for measuring moisture can lead to spending time and expense and inappropriate distribution of samples on large scales. Therefore, Remote sensing observations can be an important tool in estimating soil moisture. The present study aims to use the TOTRAM model using Landsat 8 images and the SVR method using Sentile 1 images to estimate soil moisture.Methods: In the present study, two TOTRAM methods based on pixel distribution in LST- VI space and the SVR method were used to extract soil moisture using the SAR technique and Sentinel 1 data. To implement the TOTRAM method, Landsat 8 images related to 4/29/1398 and 5/30/1398 are downloaded and after extracting NDVI and LST maps, The correlation between the dependent variable of moisture and independent temperature variables and vegetation variables has been investigated using Geographically weighted regression (GWR). To implement the SVR method after acquiring Sentinel 1 images related to 31\/05\/1398 and 27\/04\/1398, Soil Moisture Data Product FLDAS and 500 meters product of Modis Satellite (MCD12q1) were called to classify land cover in the Google Earth Engine system, and maps related to soil moisture were extracted. After extracting the moisture maps the distribution of moisture using the local Moran index has been investigated. By defining this index, positive values for this index represent the cluster of distribution.Results: Examination of the soil moisture map obtained by the SVR method showed the concentration of moisture in areas with vegetation and water and the change in moisture status from July to August was visible. The humidity pattern has shown the reflection of the precipitation pattern so that maximum precipitation and humidity were observed in April and in summer both precipitation and humidity components decreased. Examination of the TOTRAM method and application of the GWR method has shown a complete correlation between NDVI LST and moisture. However, the correlation between LST and humidity with B (values) and standard error (SE) of 0.995 and zero corresponding to July and 0.981 and zero corresponding to August showed the highest correlation with vegetation variable with moisture dependence parameter, which this correlation In August, with increasing the coefficient of determination of R2 to 0.997 and a significant decrease of NDVI to the value of 0.415 in July, it has increased much more. Application of Moran local index with values less than 0.05 for p-value and positive values for z and near positive number 1 for Moran index showed the cluster distribution of moisture variable.Conclusion: The results of TOTRAM and SVR methods showed the dependence of soil moisture status on conditions and cluster moisture distribution. According to the correlation coefficients of geographical regression, there is a greater correlation between temperature and humidity variables, especially in August, due to the decrease in vegetation density. The results of the SVR algorithm maps showed that in areas with the presence of vegetation, especially dense vegetation, we see an increase and with increasing temperature, we see a decrease in humidity. Also, the coordination of moisture patterns of the SVR algorithm and precipitation showed a direct relationship between moisture and precipitation. Considering that the SVR method uses parameters such as radar scattering intensity and land cover classification, as well as the use of Sentinel 1 radar images by this algorithm, more accurate results can be expected from this algorithm. Manuscript profile

Open Access Article

5 - The effect of kernel optimization in modeling drought phenomenon using computational intelligence (Case study: Sanandaj)
Jahanbakhsh Mohammadi Alireza Vafaeinezhad Saeed Behzadi Hossein Aghamohammadi Amirhooman Hemmasi

10.30495/girs.2023.688408

Drought is one of the most important natural disasters with devastating and harmful effects in various economic, social, and environmental fields. Due to the repetitive behavior of this phenomenon, if the appropriate solutions are not implemented, its destructive effect More

Drought is one of the most important natural disasters with devastating and harmful effects in various economic, social, and environmental fields. Due to the repetitive behavior of this phenomenon, if the appropriate solutions are not implemented, its destructive effects can remain in the region for years after its occurrence. Most natural disasters, such as floods, earthquakes, hurricanes, and landslides in the short term, can cause severe financial and human damage to society, but droughts are slow-moving and creepy in nature, and their devastating effects appear gradually and over a longer period of time. Therefore, by modeling drought, it is possible to provide plans for drought preparation and reduce the damage caused by it. In this study, computational intelligence algorithms of Multi-Layer Perceptron neural network, Generalized Regression Neural Network, Support Vector Regression with support kernel, and Support Vector regression with the proposed kernel (Support Vector) Regression New kernel has been used to model the drought using the Standardized Precipitation Index. The modeling results, in most cases, showed better performance of the proposed SVR_N model than other models. The values of RMSE and R2 were 0.093 and 0.991, respectively, and the GRNN, MLP, and SVR models performed better in modeling after SVR_N, respectively. Modeling of drought phenomenon in modeling is supported by vector regression method. Manuscript profile

Open Access Article

6 - Comparison of Linear and Non-linear Support Vector Machine Method with Linear Regression for Short-term Prediction of Queue Length Parameter and Arrival Volume of Intersection Approach for Adaptive Control of Individual Traffic Lights
mohammad ali kooshan moghadam Mehdi Fallah Tafti

IntroductionThis study was carried out in line with the development of adaptive traffic signal control systems to provide a better traffic control at intersections. In this approach, if the predicted data related to the future cycles are used to optimize the upcoming sc More

IntroductionThis study was carried out in line with the development of adaptive traffic signal control systems to provide a better traffic control at intersections. In this approach, if the predicted data related to the future cycles are used to optimize the upcoming schedule, it will control the traffic in unforeseen cases and manage it before reaching the forthcoming cycles. In order to have enough data to create such a model, the required data from two intersections in Yazd city were collected and these intersections were simulated using AIMSUN software. Then these intersections were calibrated and validated for existing conditions. The prediction accuracy results were extracted by the proposed methods and compared with the linear regression method. RMSE, MAE and GEH errors were used to compare the methods.Method: The predicted queue length and arrival volume parameters for any entry approach of itersections are major variables required during the adaptive signal control process,  Hence, Linear and Non-linear Support Vector Regression Methods combined with the time series method were used to predict these parameters. For comparison of the performance of these models with a conventional model, Linear Regression models were also developed for the prediction of these parameters.ResultsFor the developed model based on combined Linear Support Vector Regression and the time series methods, the number of optimal previous cycle data used in the model was measured as 6 and 2 previous data cycles for predicting the arrival volume at Pajuhesh and Seyed Hassan Nasrollah intersections, respectively. The optimal number of previous data used in the model was measured as 9 and 11 previous data cycles for predicting the queue length at Pajuhesh and Seyed Hassan Nasrollah intersections, respectively. Also, using the combined Non-Linear Support Vector Regression and the time series methods, the number of optimal previous data cycles was obtained as 8 and 2 cycles in predicting the arrival volume at Pajuhesh and Seyed Hassan Nasrollah intersections, and the number of optimal previous data cycles was obtained as 7 and 7 cycles in predicting the queue length at Pajuhesh and Seyed Hassan Nasrollah intersections.Discussion: The results of RMSE, MAE and GEH measures were used to compare the performance of the developed models with the real data. This comparison indicated that the model based on the combined Non-Linear Support Vector Regression and time series methods, has produced the best performance in predicting traffic arrival volume than the other aforementioned models. However, in terms of predicting the queue length, this model produced a better performance than the combined Linear Support Vector Regression at only one of the intersections. The Linear Regression model produced the weakest performance in all comparisons. Thus, it can be concluded that the combined Support Vector Regression and time series methods are appropriate tools in predicting traffic parameters in these situations. Manuscript profile

Open Access Article

7 - Support Vector Regression Parameters Optimization using Golden Sine Algorithm and Its Application in Stock Market
Mohammadreza Ghanbari Mahdi Goldani

10.22034/amfa.2021.1936352.1623

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Open Access Article

8 - A new two-phase approach to the portfolio optimization problem based on the prediction of stock price trends
Hamid Reza Yousefzade Amin Karrabi Aghileh Heydari

10.22034/amfa.2021.1937022.1622

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Open Access Article

9 - Image Resolution Increasing using Segmentation
Zahra Ghanbari Vahid Ghods

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Open Access Article

10 - Modeling and forecasting US presidential election using learning algorithms
Mohammad Zolghadr Seyed Armin Akhavan Niaki S. T. A. Niaki

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Open Access Article

11 - A prediction-based portfolio optimization model using support vector regression
Mohammad Amin Monadi Amirabbas Najafi

The purpose of portfolio optimization is to select an optimal combination of financial assets, which should be a guide for investors to achieve the highest returns against the lowest possible risk. On the other hand, one of the key factors in portfolio optimization deci More

The purpose of portfolio optimization is to select an optimal combination of financial assets, which should be a guide for investors to achieve the highest returns against the lowest possible risk. On the other hand, one of the key factors in portfolio optimization decisions is related to predict the stock prices. To do this, classical nonlinear mathematical and intelligent models such as regression are commonly used. In the present study, a nonlinear model of support vector regression with multiple outputs is applied to reduce the prediction errors. To show the effectiveness of the proposed model, the data of S & P500 index companies in the period 12/09/2016 to 02/08/2021 is used. The results show that the selection of a portfolio based on prediction using multiple vector backup regression due to considering the relationships between outputs simultaneously in terms of Sharp criteria has a better performance than the selection of portfolio based on prediction using regression method. Manuscript profile

Open Access Article

12 - Multiple Simultaneous Damage Detection in large-span bridges
محمد وحیدی آرمین عطیمی نژاد مریم فیروزی محمد هریسچیان

This paper presents a powerful two-step method for damage detection of large-span bridges with variable sections. Bridges are one of the basic infrastructures in the field of urban and suburban transportation, and timely detection of damage during its operation is impor More

This paper presents a powerful two-step method for damage detection of large-span bridges with variable sections. Bridges are one of the basic infrastructures in the field of urban and suburban transportation, and timely detection of damage during its operation is important. Damage in this category of structures will cause service disruption during natural disasters. The presented method is based on the combination of spectral finite element and modal strain energy damage index, as well as the combination of genetic algorithm and support vector regression to detect and estimate the damage severity. One of the efficient methods in the field of wave propagation is the spectral finite element method, which is capable of modeling with high flexibility and detecting micro damage. Vibration-based methods are widely used to detect structural damage, while the modal strain energy damage index has a higher sensitivity in detecting damage among other vibration-based methods. The case study model is the Crowchild Bridge in Western Canada, which has special characteristics in terms of geometry and the characteristics of structural elements. In this research, the modal strain energy damage index has been modified due to the change of cross-section along the girders. Also, support vector regression has been used as a robust technique in estimation damage severity. In order to increase the accuracy and improve the damage severity estimation method, the genetic algorithm is used to optimize the effective parameters of the support vector regression. The combined method of genetic algorithm and support vector regression has been able to estimate the severity of damages in a favorable way. Manuscript profile

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