Systematic Review On Optimization Of Artificial Neural Network For Forecasting Of Rainfall

Abstract

This systematic review reviewed a wide range of optimization approaches adopted for the enhancement of ANN performance when it comes to rainfall prediction. Chaotic, nonlinear, and high-variance time series with initial conditions sensitivity are needed to obtain accurate and precise rainfall time series data prediction. ANNS serves as a solid foundation to model these complexities. It is important to mention that predictive accuracy and reliability rely a lot on the optimization techniques selected. The paper reviews diverse optimization algorithms, ANN architectures, and data preprocessing strategies implemented for rainfall predictionThe comparative analysis demonstrates the effectiveness of the top techniques regarding the generalization capability of the deep learning model and the accuracy of forecasts. The discoveries of this review can be an excellent reference for both researchers and practitioners to optimize the models based on ANN for successful precipitation prediction. This systematic review examines various optimization approaches aimed at enhancing the performance of artificial neural networks (ANNs) in rainfall prediction. Accurate and precise predictions of rainfall time series data require the handling of chaotic, nonlinear, and high-variance time series, which are sensitive to initial conditions. ANNs provide a solid foundation for modeling these complexities; however, the predictive accuracy and reliability depend significantly on the chosen optimization techniques. The paper reviews a range of optimization algorithms, ANN architectures, and data preprocessing strategies used for rainfall prediction. It includes a comparative analysis that demonstrates the effectiveness of the best methods in terms of the generalization ability of the deep learning model and forecast errors. The findings of this review can serve as an excellent reference for both researchers and practitioners seeking to optimize ANN models for successful precipitation prediction.