A Modified Genetic Algorithm-Based Deep Learning Approach for Rainfall Category Prediction
DOI:
https://doi.org/10.54386/jam.v28i3.3219Keywords:
Rainfall prediction, Deep learning, Genetic algorithm, Artificial Neural Network, Random Forest, Machine learningAbstract
The Rainfall prediction is helpful in different fields like water management, energy supply, agriculture and others. The primary objective of rainfall prediction model is to accurately predict daily rainfall and assist in making significant decisions to tackle several challenges. In this paper, deep learning methodology has been coupled with a modified genetic algorithm to develop a Deep Learning Modified Genetic Algorithm (DLMGA) model. The goal of the model is to predict daily rainfall based on 28 years (1992 to 2020) of historical rainfall data. The performance of the DLMGA model is compared with that of the SDL (Simple Deep Learning) and DLGA (Deep Learning with Genetic Algorithm) models. An overall performance comparison of the SDL, DLGA and DLMGA models in terms of accuracy, precision, recall and f1-score reveals that the DLMGA model performs remarkably well in terms of prediction and has an impressive level of accuracy.
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