Soft Computing Models for Weather Forecasting

Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormously complex and subtle so that uncertainty in a prediction using all these parameters is enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, we analysed 87 years of rainfall data in Kerala state, the southern part of Indian Peninsula situated at latitude-longitude pairs (8 o 29′ N-76 o 57′ E). We attempted to train 5 soft computing based prediction models with 40 years of rainfall data. For performance evaluation, network predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient.