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This research addresses the challenge of predicting Water Quality Index (WQI) and Water Quality Classification (WQC), which are vital indicators for water validity. The study employed machine learning models, optimising their parameters using a grid search method. Four classification models (Random Forest, Extreme Gradient Boosting, Gradient Boosting, Adaptive Boosting) were used for WQC, and four regression models (K-nearest neighbour, Decision Tree, Support Vector Regressor, Multi-layer Perceptron) were used for WQI. Data preprocessing included imputation and normalisation. The models were evaluated using various metrics. The Gradient Boosting model achieved 99.50% accuracy for WQC prediction, while the Multi-layer Perceptron regressor model achieved a 99.8% R^2 value for WQI prediction.
Predicting water quality is crucial for protecting human health, ecosystems, and industrial processes. Accurate and timely predictions can help identify contamination risks, inform water management decisions, and ensure the safety and sustainability of water resources for various uses.
This research presents highly accurate machine learning models for predicting water quality, which could be integrated into automated water monitoring systems. Potential users include environmental agencies, water utility companies, and industrial operators needing real-time insights into water validity. This appears to be applied research, demonstrating high performance in a specific task, suggesting it is moving towards practical application.
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Abstract Water quality is very dominant for humans, animals, plants, industries, and the environment. In the last decades, the quality of water has been impacted by contamination and pollution. In this paper, the challenge is to anticipate Water Quality Index (WQI) and Water Quality Classification (WQC), such that WQI is a vital indicator for water validity. In this study, parameters optimization and tuning are utilized to improve the accuracy of several machine learning models, where the machine learning techniques are utilized for the process of predicting WQI and WQC. Grid search is a vital method used for optimizing and tuning the parameters for four classification models and also, for optimizing and tuning the parameters for four regression models. Random forest (RF) model, Extreme Gradient Boosting (Xgboost) model, Gradient Boosting (GB) model, and Adaptive Boosting (AdaBoost) model are used as classification models for predicting WQC. K-nearest neighbor (KNN) regressor model, decision tree (DT) regressor model, support vector regressor (SVR) model, and multi-layer perceptron (MLP) regressor model are used as regression models for predicting WQI. In addition, preprocessing step including, data imputation (mean imputation) and data normalization were performed to fit the data and make it convenient for any further processing. The dataset used in this study includes 7 features and 1991 instances. To examine the efficacy of the classification approaches, five assessment metrics were computed: accuracy, recall, precision, Matthews's Correlation Coefficient (MCC), and F1 score. To assess the effectiveness of the regression models, four assessment metrics were computed: Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), and coefficient of determination (R 2 ). In terms of classification, the testing findings showed that the GB model produced the best results, with an accuracy of 99.50% when predicting WQC values. According to the experimental results, the MLP regressor model outperformed other models in regression and achieved an R 2 value of 99.8% while predicting WQI values.
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DOI: 10.1007/s11042-023-16737-4
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