Rainfall Forecasting Using SSA-Based Hybrid Models with LSSVR and LSTM for Disaster Mitigation

Accurate rainfall forecasting is crucial for addressing the increasing risk of hydrometeorological disasters, particularly in tropical regions such as Semarang City, Indonesia. However, conventional forecasting models often struggle with inaccurate data and observations. This study proposes a novel hybrid combination of SSA-NMF with LSSVR and LSTM, offering high-resolution rainfall forecasting over multiple monitoring stations, to predict daily rainfall. As a preprocessing step, 15 years of daily rainfall data from six observation stations were denoised and decomposed using Singular Spectrum Analysis (SSA) combined with Non-Negative Matrix Factorization (NMF). This approach effectively handled data with many zero values, identified seasonal patterns or high-rainfall locations, and extracted key patterns. The prediction models were trained and validated using parameters optimized through RandomizedSearchCV for LSSVR and Keras Tuner for LSTM. Model performance was evaluated using MSE, RMSE, MAE, and Nash-Sutcliffe Efficiency (NSE). The results showed that the SSA-LSTM model consistently outperformed SSA-LSSVR model, with the highest average NSE value being 0.9 across six monitoring locations in Semarang City. Furthermore, the predicted rainfall values were spatially visualized using Inverse Distance Weighting (IDW) interpolation within a Geographic Information System (GIS) environment, producing informative rainfall distribution maps that support early warning systems and disaster mitigation efforts. In conclusion, the hybrid approach combining SSA-NMF preprocessing with LSTM-based deep learning significantly improves the accuracy and reliability of daily rainfall forecasting. This novel SSA‑NMF + LSSVR/LSTM framework delivers high‑resolution, reliable rainfall forecasts that directly empower disaster risk reduction systems and readily transfer to similar climatic regions.