Comparative Study of Lstm, Xgboost and Hybrid Lstm-xgboost Models for Rainfall Forecasting in Semi-arid Regions

Abstract

Rainfall forecasting remains challenging in semi-arid regions due to high variability and intermittent rainfall patterns. Statistical forecasting methods such as Autoregressive Integrated Moving Average(ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) often struggle to capture the non-linear dynamics typical of such rainfall. Machine learning (ML) techniques such as Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) networks offer improved forecasting performance but have individual limitations. This study compares LSTM, XGBoost and a hybrid LSTM-XGBoost model for monthly rainfall forecasting, using SARIMA as a baseline. The study utilizes 44 years of CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data from Tana River County, Kenya, accessed through Google Earth Engine. Data preprocessing included log transformation, stationarity testing, normalization, and feature engineering. The data was chronologically split to form the train, validation and test sets. The performance of the models was evaluated using Root Mean Square Error, Mean Absolute Error, Coefficient of Determination and Nash Sutcliffe Efficiency. Shapley Additive Explanations were used for interpretability. The XGBoost, LSTM and Hybrid LSTM-XGBoost models outperformed the baseline, achieving a Coefficient of Determination of approximately 0.61 compared with 0.52. The hybrid model performed best overall, with an RMSE of 38.68 mm, MAE of 17.23 mm, and R2 of 0.609. Hybrid forecasting approaches combining deep learning and machine learning should be further explored for rainfall prediction in semi-arid regions due to their potential to capture complex temporal patterns.