Abstract
Accessing safe water is considered a vital element of effective policy for protecting health. Numerous innovative technologies are slowly replacing human labor and other state-of-the-art methods in water quality evaluation. One of the biggest challenges faced by policy-makers and other responsible Public Health Authorities is the lack of a relatively generalizable machine learning model for water quality prediction for human consumption with provision of explanations for understanding the most influential water quality parameters. Therefore, in this paper, we proposed and applied Shapley Additive Explanations (SHAP) to study and model a robust generalizable ensemble machine learning model for water quality prediction based on water potability and other water quality metrics from various water quality samples around the world. We also calibrated our final ensemble model to achieve an accuracy score of over 90% hence making it more generalizable for water quality prediction.
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Kadiwal A (2021) Water quality
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Hellen, N., Sabuj, H.H., Ashraful Alam, M. (2023). Explainable AI and Ensemble Learning for Water Quality Prediction. In: Ahmad, M., Uddin, M.S., Jang, Y.M. (eds) Proceedings of International Conference on Information and Communication Technology for Development. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-7528-8_19
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DOI: https://doi.org/10.1007/978-981-19-7528-8_19
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