Abstract

Accurate rainfall prediction is crucial due to the complexity of precipitation patterns and their significant impact on environmental monitoring, agricultural planning, and disaster management. Reliable forecasts are essential for farmers to improve crop yields and for the efficient management of water resources, including water harvesting and infrastructure planning. However, challenges persist in modeling natural disasters and predicting heavy rainfall because of the variability and non-linearity of meteorological patterns. In this study, historical weather data from southern Saudi Arabia were collected using a live database containing multiple meteorological parameters. A hybrid deep learning model is proposed that integrates a generalized regression neural network (GRNN) with an encoder–decoder bidirectional long short-term memory (EDBi-LSTM) network. To fine-tune the model parameters effectively, a novel hybrid optimization technique is employed, combining the grey wolf optimizer (GWO) with moth-flame optimization (MFO). The model’s performance is evaluated using several metrics, including sensitivity, specificity, f-score, accuracy, recall, and error rate. Experimental results demonstrate that the proposed approach achieves higher accuracy and robustness in rainfall prediction compared to traditional methods.

Keywords

Rainfall prediction, Deep learning, GRNN, EDBi-LSTM, Hybrid optimization, Meteorological data.

Cite this article

Devi BK, Manjunatha, Thirumalraj A, Sharma T. Fine-tuned GWOMFO-based hybrid deep learning approach for rainfall prediction.International Journal of Advanced Technology and Engineering Exploration.2025;12(131):1-13

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