Prediction and Evaluation of the Efficiency of MLP and ANFIS Artificial Neural Networks for Estimating Annual and Monthly Precipitation and Temperature in the Western Iran

摘要

Precipitation and temperature are among the basic climate variables affecting all areas, especially in agriculture and water resources management. Quantitative changes of these variables at any time scale and outside the estimated normal range can affect the two mentioned general areas in terms of water resource allocation planning. In this study, the effectiveness of Multilayer Perceptron Artificial Neural Network (MLP-ANN) with Levenberg-Marquardt training algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian membership function were used in modeling and forecasting of annual and monthly precipitation and temperature in western Iran and in the geographical range of Kermanshah, Ilam, Lorestan, Kurdistan, and Hamadan provinces under different scenarios was studied. For this purpose,The precipitation and temperature data of 16 synoptic meteorological stations with statistical period of at least thirty years were used. After creating a database tailored to the project goals, the quality and accuracy of the statistical data of the stations, and the existence of outliers were evaluated. Findings were obtained based on statistical preference indices such as MSE, MAE, and NS (NashSutcliffe); and the projected outputs of the next 5 years were compared with the mean change of the data. The results indicated that Multilayer perceptron with different scenarios of the number of input layer neurons, hidden layer neurons, and the related neurons compared to ANFIS with its own scenarios consisting of the number of input layer neurons and clusters or membership functions, in spite of the inherent differences in precipitation and temperature variables and in terms of the studied time scale, is more capable of adapting to the data and providing estimation models; That is, more than 95% of the quadratic variables of all stations were modeled using a different range of criteria (NS = 0.2626 - 0.9884). However, in ANFIS method, about 63% of variables with statistical index range (NS = 0.3241 - 0.9841) were able to give a positive response to the modeling. In addition, the results of both methods showed that the preference index value for temperature parameters was more than the precipitation parameters and annual precipitation index was better than monthly precipitation index and the preference index value for monthly temperature was better than the annual temperature. The important point in evaluating the results of each method is that a mere cite to the values of the preference statistical index, especially for data with seasonal fluctuations, without considering the predicted data and comparing them with the general time series variations, may lead to serious errors in conclusions and disruption of a proper model presentation.