Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction

Abstract

ABSTRACTRainfall prediction is one of the crucial stages of the watershed management process. In this research, A comparison of the performance among Monte Carlo and Thomas Fiering, linear regression (LR), multiple linear regression (MLR), and SVM optimized by Simulated Annealing (SVM-SA) is carried out for Monthly rainfall prediction. In addition, the efficiency of the input patterns to the models including single input-multiple output (SIMO), multiple input-multiple output (MIMO), single input-single output (SISO), multiple input-single output (MISO) patterns are investigated. For this purpose, the time series of 34 rain gauge stations in the Karkheh basin was used. The results showed that SISO, MISO, MIMO, SIMO, and Monte Carlo and Thomas Fiering models are ranked first to fifth respectively. By comparing the performance of the models, it can be found that there is no significant difference between the SVM-SA, LR, and MLR models, However, the LR model is a method for predicting monthly rainfall more easily than other methods. This method has fewer adjustable parameters than other models.

Keywords

• Artificial intelligence
• prediction
• rainfall
• support vector machine