Prediction of long-term monthly precipitation using several soft computing methods without climatic data

Accurate estimation of precipitation is an important issue in water resources engineering, management and planning. The accuracy of four different soft computing methods, adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), artificial neural networks (ANN) and support vector regression (SVR), is investigated in predicting long-term monthly precipitation without climatic data. The periodicity component, longitude, latitude and altitude data from 50 stations in Iran are used as inputs to the applied models. The ANFIS-GP model is found to perform generally better than the other models in predicting long-term monthly precipitation. The SVR model provides the worst estimates. The maximum correlations are found to be 0.935 and 0.944 for the ANFIS-SC and SVR models in Fasa station, respectively. The highest correlations of the ANFIS-GP and ANN models are found to be 0.964 and 0.977 for the Bam and Tabas (Zabol) stations. The minimum correlations are 0.683 and 0.661 for the ANFIS-GP and SVR models in Urmia station while the ANFIS-SC and ANN models provide the minimum correlations of 0.696 and 0.785 in the Sari and Bandar Lengeh stations, respectively. The comparison results show that the long-term monthly precipitations of any site can be successfully predicted by ANFIS-GP model without any weather data. The monthly and annual precipitations are also mapped and evaluated by using the optimal ANFIS-GP model in the study. The precipitation maps revealed that the highest amounts of precipitation occur in the north, southwestern and west regions, while the lowest values are seen in the east and southeastern parts of the Iran.