Background and Objective: Predicting the results of the implementation of strategic air pollution control policies is the first and most important challenge for Tehran municipality. The main objective of this study was to define a specific method for assessing the resul More
Background and Objective: Predicting the results of the implementation of strategic air pollution control policies is the first and most important challenge for Tehran municipality. The main objective of this study was to define a specific method for assessing the result of urban air pollution controlling strategies in Tehran metropolis using a multi-dimensional decision support system. Method: First, the most appropriate air pollution control strategies were selected based on existing conditions and structures in each zone of the city and then weighed according to selected criteria. Based on the spatial monitoring of air pollution formation patterns in the past and present time, as well as the analysis of their effects, the results of implementing air pollution control strategies were simulated using Geo-Artificial Neural Network models. In the next step, variables of time series and uncertainty variables were simulated for predicting the potential future air pollution patterns and finally, the results of the defined control strategies were evaluated based on spatial thematic layers. Findings: Definition of final clusters of air quality control strategies, weighting and ranking of the selected policies based on defined criteria have been the first findings of this research. Also, extraction of time series zoning based on the data collected during a four-year period, as well as simulation of the baseline scenario models and spatial data layers of their output were among the achievements of this study. Finally, the modeling of the predictive variables, design of the air quality control software and the prediction of the results of the the implementation of air pollution control strategies were presented. The results showed that by applying the Geo-Artificial Neural Network models (GANN), the urban managers could effectively predict the results of implementing the air pollution control strategies. Discussion and Conclusion: The results of this study showed that the spatio-temporal analysis supports the process of evaluation and prediction of the effects of pollution and can be used to determine the best pollution control strategies for the zones affected by air pollution. The final results of GANN models indicate that if the selected strategies are implemented based on the scenarios defined, in the "optimistic scenario", air quality in all areas of Tehran is completely stable and remains healthy, while in the "ordinary scenario" will reduce the level of air pollution up to 70 percent in the autumn and winter season if the selected strategies are implemented compared to the lack of implementation of control plans. The final model of the verification process model also confirmed that the pattern of pollution predicted by the model in each of the urban areas had a proper trend and adaptation compared to the pattern of contamination obtained from the actual results of the field data.
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The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its More
The purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its location is the Tehran Stock Exchange and during the years 2010 to 2020. Findings showed that the prediction error rate with artificial neural network has a higher accuracy than Markov's hidden model. Also, the prediction error of the hybrid model is much lower than the other two models for predicting the total stock index of Tehran Stock Exchange, so it has higher accuracy for forecasting stocks. According to the MAPE index, the hybrid model method could improve the predictive power of the artificial neural network by 0.044% and also improve the predictive power of the hidden Markov model by 0.70%.
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For more accurate measurement of the water flow, it has been always attempted to design structures with least errors and highest accuracy. Nowadays, the use of artificial neural networks (ANN) models has been rapidly grew mainly due to the fact that these models are not More
For more accurate measurement of the water flow, it has been always attempted to design structures with least errors and highest accuracy. Nowadays, the use of artificial neural networks (ANN) models has been rapidly grew mainly due to the fact that these models are not confined to the physical parameters. Artificial neural networks are based on use of embedded knowledge between input and output variables of a problem, regardless of physical aspects and these networks are able to extract inherent relation of the input and output and they can generalize the obtained relation to other situations and cases. In the present research, the information related to the overflow of Marun Storage Dam was adopted. The input parameters of ANN model are as follows: day, month, water surface elevation, water sharing percent and output parameters overflow discharge of storage dam. The models employed in artificial neural networks include FF, JEN, MLP and RBF. Moreover, the genetic algorithm (GA
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