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Articles by M.T. Dastorani
Total Records ( 2 ) for M.T. Dastorani
  M.T. Dastorani , A. Talebi and M. Dastorani
  Research on the application of artificial neural networks to the prediction of runoff from ungauged catchments is presented. Available catchment descriptors have been used as input data and the index flood as output. Different types and numbers of catchment descriptors were used to ascertain which gave the best relationship with the hydrological behavior and flood magnitude. Different architectures of ANN were developed and evaluated. Results show that the selection of pooling groups of catchments either randomly or according to geographical proximity does not produce desirable results. Therefore, hydrologically similar catchments were clustered using the Flood Estimation Handbook software and this improved the accuracy of the predictions. Finally, a comparison of the ANN approach and the Flood Estimation Handbook is described that shows the advantages of the ANN approach.
  M.T. Dastorani , H. Afkhami , H. Sharifidarani and M. Dastorani
  The purpose of this research is to evaluate the applicability of two artificial intelligence techniques including Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in prediction of precipitation amount before its occurrence. In fact, this paper presents the application of these models to predict precipitation in Yazd meteorological station in central Iran with a hyper arid climate condition and very low and highly variable annual rainfall. In this study, different architectures of ANN and ANFIS models as well as various combinations of meteorological parameters including 3-year precipitation moving average, maximum temperatures, mean temperatures, relative humidity, mean wind speed, maximum wind direction and evaporation have been used as inputs of the models. According to the results, among different architectures of ANN, dynamic structures including Recurrent Network (RN) and Time Lagged Recurrent Network (TLRN) showed better performance for this application. Final results show that the efficiency of TLRN and ANFIS for this application are almost the same, although in different tests with different input patterns the results produced by these two methods are slightly different. In general, it was found that both ANN and ANFIS models are efficient tool to model and predict precipitation amounts 12 months in advance.
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