List of Articles شبکه بیزین

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  1 - Rehabilitation of Aquatic Ecosystems Based on environmental water rights upstream of Water Reservoirs with Inlet Flow Prediction Approach (Case Study: Taleghan Dam Basin)
  Zahra Nafariyeh Mahdi Sarai Tabrizi Hossein Babazadeh Hamid Kardan Moghaddam
  10.30495/jrnr.2022.69887.10263
  Limited water resources and increased water demand in recent decades have caused irreparable damage to the country's water resources. One of the important components in surface water optimization and management is long-term and short-term river flow forecasts. The aim o More

  Limited water resources and increased water demand in recent decades have caused irreparable damage to the country's water resources. One of the important components in surface water optimization and management is long-term and short-term river flow forecasts. The aim of the present study is to compare the performance of two Bayesian BN network models with probabilistic approach and MLP neural network. Then selecting the best structural model for flow prediction is another goal of the present study. Monthly meteorological data including precipitation, monthly average temperature, evaporation and. Also, the volume of water transferred from five hydrometric stations entering the Taleghan Dam from 2006 to 2018 was introduced as input data to the models. and runoff to the dam was considered as predictable. Then, with the aim of estimating the best Prediction pattern structure, Input data with different layouts were introduced to the models. In the next step, using the hydrological method of Tennant, The environmental discharge was calculated And the probability of these discharges occurring in the registration data and seventeen patterns in the Easyfit software environment was calculated. Then comparing the selected pattern according to the probability of occurrence and the criteria of the index, Nash-Sutcliffe coefficient (NS), root mean square error (RMSE) and mean absolute prediction error (MAPE) was performed. The best model in BN model with 43.3% similarity and index criteria were estimated to be -3.98, 300, 17.3 and 0.06, respectively. MLP model with 80% similarity and index criteria were introduced as -10.3, 8266, 23.9 and 122.3 in the best model, respectively. As a result, both models performed well in runoff estimation, but comparing the environmental probabilities of the two models in the top five patterns, the BN model has an acceptable accuracy . The basin was also found to be at environmental risk. Manuscript profile
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  2 - Assessment of Intelligent models for Estimating the Electrical Conductivity in Groundwater (Case study: Mazandaran plain)
  Isa Hazbavi Reza Dehghani
  10.22034/jest.2018.13753
  Abstract Background and Objective: Groundwater resources along with surface water supply the needs for municipal, industrial and agriculture uses, and their quantity and quality should be investigated. Salinity is one of the most important parameters in assessing the qu More

  Abstract Background and Objective: Groundwater resources along with surface water supply the needs for municipal, industrial and agriculture uses, and their quantity and quality should be investigated. Salinity is one of the most important parameters in assessing the quality of groundwater. Method: In this study, application of artificial neural networks and Bayesian network in predicting the electrical conductivity in 8 observation wells in Mazandaran plain was investigated. For this purpose, hydrogen carbonate, chloride, sulfate, calcium and magnesium were selected as input and output parameters for electrical conductivity at monthly a scale during 2003-2013. The criteria of correlation coefficient, mean absolute error and Nash Sutcliff coefficient were used to evaluate the performance of the model. Findings: The results showed that artificial neural network model has the highest correlation coefficient (0.989), the lowest mean absolute error (0.019 ds/m) and the highest standard of Nash Sutcliffe (0.970) ranked the first priority in the validation phase. Discussion and Conclusion: The results indicate acceptable capability of artificial neural network models to estimate the electrical conductivity of groundwater.   Manuscript profile
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  3 - Comparing the Application of Bayesian Modeling and Multi Criteria Decision Making Method in Environmental Risk Assessment of Dams (Case study: Taleghan Dam)
  Negar Tayebzadeh Moghadam Bahram Malekmohammadi Ahmadreza Yavari
  10.22034/jest.2017.10742
  Background and Objective: Environmental risk assessment is an important tool to achieve sustainable development. The purpose of this study is application of Bayesian modeling method based on a hierarchical structure for prioritization, assessment and offering management More

  Background and Objective: Environmental risk assessment is an important tool to achieve sustainable development. The purpose of this study is application of Bayesian modeling method based on a hierarchical structure for prioritization, assessment and offering management solutions to reduce the hazards of Taleghan dam environmental risks. Method: In the first method, environmental risk assessment (ERA) of Taleghan dam was performed by using Bayesian Network (BN) and the Netica software. To compare the results of this method with those of conventional methods such as multi criteria decision making method (MCDM), ERA of Taleghan dam was also performed by MCDM method and use of the Expert Choice software. Findings: Based on the obtained results, the output node of the BN, changes in land use, effects on population and erosion and sedimentation are the most important risks and pollution, seismic, flooding, tourism and ecosensetivity are in second priorities. Conclusion: BN as a new method with some advantages such as considering the relation between variables and uncertainty conditions data is considered flexible model with high capacity for ERA. Therefore, to achieve a comprehensive solution for environmental risk of engineering projects such as dam construction, application of BN based on the MCDM has a high performance.   Manuscript profile
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  4 - Optimal Portfolio Selection using Machine Learning Algorithms
  Mohammad baghar yazdani khodashahri Seyed Hossein Naslemousavi Mir Saeid Hoseini Shirvani
  Choosing the right portfolio is always one of the most important issues for investors. The price trend is predicted using technical analysis or basic analysis. Technical analysis focuses on market performance, while the focus of fundamental analysis is on the mechanism More

  Choosing the right portfolio is always one of the most important issues for investors. The price trend is predicted using technical analysis or basic analysis. Technical analysis focuses on market performance, while the focus of fundamental analysis is on the mechanism of supply and demand, and these changes prices. The existence of a solution to predict growth or decrease in stocks has been studied as a basic need in this study. In the present study, with the help of a monitoring dataset, a solution based on Raff collection algorithms and hierarchical analysis to reduce the feature and decision tree algorithms, backup vector machine, and business network have been used for prediction. This proposed solution has been implemented using language and compared with different solutions, and the research results have shown that the proposed method with 80% accuracy of prediction and 20 errors in prediction has the highest accuracy and the lowest error rate among the methods compared. Manuscript profile
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  5 - جبران داده‌های مفقود پزشکی با ترکیب شبکه بیزین و ماشین یادگیری مفرط
  الهه صباحی سید محمد حسین معطر رضا شیبانی
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  6 - Iran Stock Market Prediction Based on Bayesian Networks and Hidden Markov Models
  Zohreh Alamatian Majid Vafaei Jahan
  Stock market behavior is one of the most complex mechanisms, considered by researchers. Financial markets are influenced by the external and internal factors. External factors such as political and social factors are not measurable, so prediction the trend of stock mark More

  Stock market behavior is one of the most complex mechanisms, considered by researchers. Financial markets are influenced by the external and internal factors. External factors such as political and social factors are not measurable, so prediction the trend of stock markets is focused on internal factors. This study suggests a hybrid approach based on Bayesian Networks and Hidden Markov Models to predict trend of stock market. The used variables are 6 index of Tehran Stock Exchange, which have the most correlation coefficient with target stock, and 22 technical indicators. Bayesian networks are utilized to find the relationships between variables, and the effect of each variable in prediction considered from conditional probability tables. Hidden Markov Model is designed for sets of extract from Bayesian networks. The proposed model tested on four company’s stock names Mobarakeh Steel, Iran Khodro, Mellat Bank and Iran drug. The average accuracy of the proposed system is 83.26 %. The experimental results show that the suggested procedure has higher performance for prediction of stock markets in comparison with other previous methods. Manuscript profile