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Uncertainty Analysis of Predictive Models for Water Quality Index: Comparative Analysis of XGBoost, Random Forest, SVM, KNN, Gradient Boosting, and Decision Tree Algorithms.
Abbas, F.; Cai, Z.; Shoaib, M.; Iqbal, J.; Ismail, M.; Arifullah; Alrefaei, A.F.; Albeshr, M.F. Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan. Water2024, 16, 941.
Abbas, F.; Cai, Z.; Shoaib, M.; Iqbal, J.; Ismail, M.; Arifullah; Alrefaei, A.F.; Albeshr, M.F. Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan. Water 2024, 16, 941.
Abbas, F.; Cai, Z.; Shoaib, M.; Iqbal, J.; Ismail, M.; Arifullah; Alrefaei, A.F.; Albeshr, M.F. Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan. Water2024, 16, 941.
Abbas, F.; Cai, Z.; Shoaib, M.; Iqbal, J.; Ismail, M.; Arifullah; Alrefaei, A.F.; Albeshr, M.F. Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan. Water 2024, 16, 941.
Abstract
Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity to appraise water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that random forest and gradient boosting eclipsed other algorithms, achieving an accuracy rate of 99%. In close pursuit were SVM and XGBoost, registering accuracy scores of approximately 95% and 93%, respectively, while KNN and Decision Trees garnered accuracy rates of 88% and 87%, respectively.
In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization
Keywords
groundwater modelling; water quality index; machine learning algorithms; water quality assessment
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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