Rainfall Forecasting Using Soft Computing Models and Multivariate Adaptive Regression Splines

Long-term rainfall prediction is a challenging task especially in the modern world where we are facing the major environmental problem of global warming. In general, climate and rainfall are highly non-linear phenomena in nature exhibiting what is known as the "butterfly effect". While some regions of the world are noticing a systematic decrease in annual rainfall, others notice increases in flooding and severe storms. The global nature of this phenomenon is very complicated and requires sophisticated computer modelling and simulation to predict accurately. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies [17] that underlie the conception, design and utilization of intelligent systems . In this paper, the SC methods considered are i) Evolving Fuzzy Neural Network (EFuNN) ii) Artificial Neural Network using Scaled Conjugate Gradient Algorithm (ANNSCGA) iii) Adaptive Basis Function Neural Network (ABFNN) and iv) General Regression Neural Network (GRNN). Multivariate Adaptive Regression Splines (MARS) is a regression technique that uses a specific class of basis functions as predictors in place of the original data. In this paper, we report a performance analysis for MARS [1] [16] and the SC models considered. To evaluate the prediction efficiency, we made use of 87 years of rainfall data in Kerala state, the southern part of the Indian peninsula situated at latitude-longitude pairs (829' N 7657' E). The SC and MARS models were trained with 40 years of rainfall data. For performance evaluation, network predicted outputs were compared with the actual rainfall data for the remaining 47 years. Simulation results reveal that MARS is a good forecasting tool and performed better than the considered SC models.