Title: Neural Network-Based Water Quality Prediction
Authors: Mohammed Ashraf Al-Madhoun and Samy S. Abu_Naser
Volume: 7
Issue: 9
Pages: 25-31
Publication Date: 2023/09/28
Abstract:
Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water quality monitoring and decision-making for stakeholders and policymakers.