Regional Rainfall Forecasting using Large Scale Climate Teleconnections and Artificial Intelligence Techniques

Abstract

This paper presents an Artificial Intelligence approach for regional rainfall forecasting for Orissa state, India on monthly and seasonal time scales. The possible relation between regional rainfall over Orissa and the large scale climate indices like El-Niflo Southern Oscillation (ENSO), EQUitorial INdian Ocean Oscillation (EQUINOO) and a local climate index of Ocean-Land Temperature Contrast (OLTC) are studied first and then used to forecast monsoon rainfall. To handle the highly non-linear and complex behavior of the climatic variables for forecasting the rainfall, this study employs Artificial Neural Networks (ANNs) methodology. To optimize the ANN architecture, Genetic Optimizer (GO) is used. After identifying the lagged relation between climate indices and monthly rainfall, the rainfall values are forecast for the summer monsoon months of June, July, August, and September (JJAS) individually, as well as for total monsoon rainfall. The models are trained individually for monthly and for seasonal rainfall forecasting. Then the trained models are tested to evaluate the performance of the model. The results show reasonably good accuracy for monthly and seasonal rainfall forecasting. This study emphasizes the value of using large-scale climate teleconnections for regional rainfall forecasting and the significance of Artificial Intelligence approaches like GO and ANNs in predicting the uncertain rainfall.