Optimizing silt density index prediction in water treatment systems using pressure-based gradient boosting hybridized with Salp Swarm Algorithm

This study introduces a novel approach for predicting Silt Density Index (SDI) values in desalination systems by combining Gradient Boosting with the Salp Swarm Algorithm (GBSSA). The integration of GBSSA enhances predictive accuracy over a one-month period, addressing critical challenges related to water scarcity exacerbated by climate change. Data were collected from two desalination plants in western Algeria, specifically the Benisaf and El Mactaa plants, to train and validate the models. Pressure measurements were chosen as input features due to their ease of measurement and the use of simple, cost-effective equipment, minimizing the need for complex sensors and reducing manual intervention. The study compared the performance of three Gradient Boosting variants: the standard Gradient Boosting model (GB), Gradient Boosting optimized with Particle Swarm Optimization (GBPSO), and the proposed GBSSA model. Results demonstrated that the GBSSA model significantly outperformed both GB and GBPSO, achieving superior accuracy in predicting SDI values. This enhanced predictive capability is crucial for effective monitoring and management of fouling in desalination plants, facilitating better operational adjustments and maintenance decisions. The findings underscore the potential of combining advanced machine learning techniques with optimization algorithms to improve predictive modeling in critical environmental contexts.