Rider exploitation based whale optimization algorithm for rainfall prediction from meteorological data

The rainfall prediction is one of the major problems in meteorological department since it is related to the economy and human life. In addition, the heavy rainfall is the major problem of natural disasters like drought and flood. The accuracy of the rainfall forecasting has been spotted for countries like India and economy is greatly dependent by agriculture. This paper introduces a new rainfall prediction model consists of Feature extraction and Prediction. The features are extracted by modified Empirical Mean Curve Decomposition namely Empirical Mean Median Curve Decomposition, and then subjected to statistical analysis. The results of the statistical features are processed by the classification process. Neural network (NN) is used for the rainfall prediction. To improve the accuracy of proposed work, an optimal weight is and predicted result are multiplied, then, the weight in NN is optimally selected by a Rider Exploitation based Whale Optimization Algorithm. Finally, the performance of the proposed work is compared and proved over the state‐of‐the‐art models in terms of prediction rate.