Precision water quality indices forecasting through an optimized hybrid SMW-LSSVM-R model enhanced by SATLDE and uncertainty analysis

Precise forecasting of water quality indices (WQI) is essential for safeguarding ecosystems, human health, and sustainable water resource management. This study presents an innovative approach for evaluating river Water Quality Indices using advanced machine learning methods. The approach combines the least squares support vector machine (LSSVM) with the Sherman–Morrison–Woodbury (SMW) formula and local weighting techniques to improve the model's capacity to identify local trends and nonlinearities. The hybrid model, SMW-LSSVM-R, integrates the advantages of SMW-LSSVM with ridge regression to provide a balanced and resilient predictive framework. The model parameters are improved by a self-adaptive teaching-learning-based differential evolution (SATLDE) method, attaining optimal performance. Additionally, SATLDE is combined with a ridge feature selection model to identify the key input factors and boost accuracy. The model also employs optimized multivariate variational mode decomposition (OMVMD) using SATLDE algorithm to more effectively assess complex data patterns. When the models were tested at two Iranian stations, Farisat and Molasani, the SMW-LSSVM-R model with a testing R value of 0.975 and an RMSE of 0.990, exhibited better performance than the basic and OMVMD-enhanced models. These findings demonstrate the potential of the proposed hybrid model to offer valuable insights into environmental monitoring and management.