Hybrid least squares support vector machine for water level forecasting
Previous studies have highlighted the significant role of historical water level data in flood forecasting. In this study, we compare two standalone models, Support Vector Machine (SVM) and Least Squares Support Vector Machine (LSSVM), with hybrid models that integrate Ensemble Empirical Mode Decomposition (EEMD) with SVM and LSSVM, aiming to develop a more effective forecasting approach for hydrological data. Particle Swarm Optimization (PSO) is incorporated into these hybrid models to optimize the parameters of SVM and LSSVM, resulting in four models: SVM-PSO, LSSVM