Advanced monthly rainfall forecasting in Southern Lazio: Integrating climatic indices, classical or deep neural networks, and a feature selection algorithm

Accurately predicting monthly precipitation is crucial for managing water resources, particularly in regions with complex climates like Southern Lazio, Italy. This study addresses the challenges of selecting relevant climate parameters and optimizing model complexity for precise forecasting. Four models, based on Bi-directional Long Short-Term Memory (BI-LSTM) and Radial Basis Function (RBF) neural networks, were compared, focusing on their ability to predict monthly precipitation using diverse inputs such as mean temperature, relative humidity, sea level pressure, radiation, precipitation change, and 12 climate indices. One significant challenge was determining the most impactful subset of input variables from a large dataset to enhance model performance without overfitting. To address this, the Particle Swarm Optimization (PSO) feature selection algorithm was employed, significantly improving model accuracy by identifying the optimal subset of input variables. The comparative analysis revealed that PSO-based models consistently outperformed those using all input variables, with the PSO-RBF model achieving a Kling-Gupta Efficiency (KGE) value of up to 0.80 during testing. Notably, both PSO-BI-LSTM and PSO-RBF models demonstrated strong performance in predicting precipitation peaks, achieving KGE values of 0.74. The study highlights the efficacy of the RBF model combined with PSO, which optimizes fewer parameters compared to the more complex BI-LSTM network while reducing input variables. This approach is not only less computationally intensive but also easier to implement, particularly in regions with limited instrumentation. These findings suggest that the PSO-RBF model is a reliable and efficient tool for monthly precipitation prediction in challenging environments.