Advancing Monthly Rainfall Predictions: Multi-Step Forecasting with Hybrid ACO and GBDT Model

The process of determining when precipitation is likely is exceedingly complex. It may be challenging to model the reaction using conventional approaches for rainfall time series simulations because of the complexity of hydrologic processes and the participation of many fundamentally complex variables, such as meteorological and geomorphologic components. This approach consists of three stages, which are preprocessing data, training the model, and normalization. One way to approximate the original variables is by using the principal component analysis (PCA) method, which uses all of the original variables to obtain a smaller set of PCs. To make all of the climate parameters have the same value, we apply a normalization technique. During the entire process of training the model, we utilized an ACO-GBDT. With an average accuracy of 93.67%, this innovative approach surpasses GBDT and ACO.