Forecasting water quality through machine learning and hyperparameter optimization

Abstract

Forecasting water quality through machine learning and hyperparameter optimization is a research endeavor aimed at enhancing the water quality prediction process. The primary goal of this study is to employ various machine learning algorithms for water quality prediction and to refine existing models from previous research. The paper encompasses a comprehensive literature review of previous water quality prediction studies and introduces novel theoretical insights. The research employs a classic machine learning problem-solving approach, predominantly utilizing the extreme gradient boost (XGBoost) algorithm. Additionally, it evaluates other machine learning algorithms, including the random forest (RF) classifier, decision tree (DT) classifier, adaptive boosting (AdaBoost) classifier, support vector machine (SVM), Naïve Bayes, and extra tree classifier for comparison. The evaluation process utilizes a classification report, providing insights into the precision, recall, f1-score, and accuracy of each machine learning model. Notably, the XGBoost model exhibits superior performance, achieving an impressive 97.06% accuracy. Precision stands at 94.22%, recall at 81.5%, and F1-score at 87.4%. These results represent a significant advancement over prior water quality prediction models, emphasizing the potential of machine learning and hyperparameter optimization to enhance water quality forecasting in environmental monitoring.

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“…Its classification effectiveness has been demonstrated through high accuracy fast, speed, and reliable algorithm, particularly when handling large datasets. To define the NB technique, it's crucial to comprehend the categorization procedure necessitates set indicators for ascertain the suitable category for the examined sample (Alamsyah & Salma, 2018;Aini & Mustafa, 2020;Yarragunta et al, 2021;Tangwannawit & Tangwannawit, 2022;Elvin, 2024). On the other hand, NB algorithm is a probabilistic classifier.…”

“…This algorithm utilizes supervised learning to address classification and regression problems. In previous study, the method of DT used for both classification and predictions, is structured with leaf, root, and decision nodes (Alamsyah & Salma, 2018;Krishna et al, 2023;Simu et al, 2020;Benifa et al, 2022;Aram et al, 2024;Elvin, 2024;Yarragunta et al, 2021;Kang et al, 2018). Positioned at the top are the root nodes, while the leaf nodes are in the middle and decision nodes at the bottom.…”

“…The RF algorithm, employed in supervised learning, is versatile for both classification and regression tasks. By utilizing averaging methods, the RF algorithm enhances prediction accuracy and helps mitigate the risk of overfitting (Elvin, 2024). Random Forest is a collection of trees built independently using different bootstrap samples from a dataset.…”

“…In the RF, each node is split using the optimal splitter chosen from a subset of predictors. At every node, random predictors are utilized, and this element of randomness offers overfit protection (Alamsyah & Salma, 2018;Schonlau & Zou, 2020;Yarragunta et al, 2021;Hai et al, 2022;Benifa et al, 2022;Ravindiran et al, 2023;Baladjay et al, 2023;Gupta et al, 2023;Elvin, 2024;Aram et al, 2024). When presented with new data, each DT makes its own prediction.…”