Hybrid model for rainfall prediction with statistical and technical indicator feature set

Abstract

As excessive rain may cause numerous disasters, rainfall prediction is very crucial and the prediction should be realistic since it encourages individuals to take preventative steps. This work proposes a new rainfall prediction model by following three major phases: Preprocessing, Extraction of Features and Prediction. The Improved Data Normalization technique is carried out for preprocessing the data. Then, it extracts the technical Indicator features like Average Directional Movement (ADX), Moving Average Convergence Divergence (MACD), RSI, Welles Wilder's Smoothing Average (WWS), statistical & higher order statistical features. According to these features, prediction takes place by a hybrid model that includes the Improved DBN & LSTM methods. By fine-tuning the best weights, the training will be carried out optimally to increase the prediction model's efficacy. For this, the Brownian Motion-based Pelican Optimization Algorithm (BMPOA) is implemented. Finally, the proposed technique has contrasted over existing models concerning different metrics.