Water Classification Based On Mineral Content by Psobpnn

Abstract

Water is the very essence and the most invaluable natural resource. Over the recent decades, the degradation of water quality has been notable, primarily attributed to pollution and various other challenges. This has created a pressing demand for a model capable of providing precise forecasts regarding water quality. This research paper introduces a novel methodology for water classification based on mineral content, leveraging the Particle Swarm Optimization-enhanced Back propagation Neural Network (PSOBPNN). The study focuses on accurately categorizing water samples into distinct classes by analyzing their mineral composition, aiming to contribute to the field of water quality assessment. The proposed PSOBPNN model is employed to effectively learn and discern patterns in the mineral composition of water samples. The integration of Particle Swarm Optimization with the Back propagation Neural Network enhances the model's optimization capabilities, facilitating accurate and efficient convergence to optimal solutions. The experimental results showcase the effectiveness of the PSOBPNN model in achieving a high level of accuracy in water classification based on mineral content. The study underscores the potential significance of this approach in environmental monitoring, emphasizing the importance of considering mineral composition as a key determinant of water quality.