Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models

Abstract

This study investigates the performance of the M5Tree model, multivariate adaptive regression spline, least square support vector regression (LSSVR), gene expressing programming (GEP) and artificial neural networks methods in estimating monthly long-term rainfall. Data from 61 rain stations in Madhya Pradesh and Chhattisgarh states, Central India, were used in the applications. Geographical inputs and periodicity were used as inputs to the models, and the methods were compared with each other according to the determination coefficient (R2), mean absolute errors (MAE) and root-mean-square errors (RMSE). LSSVR was found to be the best model with RMSE = 13.93 mm, MAE = 9.52 mm and R2 = 0.995 while the GEP provided the worst results with RMSE = 36.74 mm, MAE = 29.89 mm and R2 = 0.955 in prediction of long-term rainfall in the test stage. The lowest RMSE (5.53 mm) and MAE (3.89 mm) were obtained for the Rajnandgaon Station, while the Raigarh Station provided the worst accuracy (RMSE = 31.8 mm and MAE = 21.56 mm) for LSSVR model.