Water Quality Index Classification Based on Machine Learning: A Case from the Langat River Basin Model

Abstract

Traditionally, water quality is evaluated using expensive laboratory and statistical procedures, making real-time monitoring ineffective. Poor water quality requires a more practical and cost-effective solution. Water pollution has been a severe issue, hurting water quality in recent years. Therefore, it is crucial to create a model that forecasts water quality to control water pollution and inform consumers in the event of the detection of poor water quality. For effective water quality management, it is essential to accurately estimate the water quality class. Motivated by these considerations, we utilize the benefits of machine learning methods to construct a model capable of predicting the water quality index and water quality class. This study aims to investigate the performance of machine learning models for multiclass classification in the Langat River Basin water quality assessment. Three machine learning models were developed using Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM) to classify river water quality. Comparative performance analysis between the three models indicates that the SVM is the best model for predicting river water quality in this study. In addition, there is a statistically significant difference in performance between the SVM, DT, and ANN models at the 0.05 level of confidence. The use of the kernel function, the grid search method, and the multiclass classification technique used in this study significantly impacts the effectiveness of the SVM model. The findings bolster the idea that machine learning models, particularly SVM, can be used to forecast WQI with a high degree of accuracy, hence enhancing water quality management. Consequently, the model based on machine learning lowered the cost and complexity of calculating sub-indices of six water quality parameters and classifying water quality compared to the standard IKA-JAS formula.

Comparison of river water quality assessment methods using the tree model and the nearest neighbor method (A case study: AhvazHydrometric Station)

“…The ndings of other researchers who have worked on the water quality of different rivers reinforce the idea that machine learning models can be used to predict water quality assessments with a high degree of accuracy. As a result, machine learning-based models reduce the cost and complexity of calculating subindices of water quality parameters and hence improve water quality management) Shamsuddin et al 2022;Malek et al 2022 ; Khoi et al 2022). The Karoon River Basin is the largest river basin in Iran.…”

Review of Malaysia's Environmental Waterways' Compliance with Industrial Effluent Discharge

“…It is globally known that water is the main basic resource for human survival and environmental biodiversity. Water quality has been affected by water pollution that clearly an over aching issue (Shamsuddin et al, 2022). There are numerous instances of water pollution brought on by industrial discharges that have resulted in Malaysian water treatment facilities closing down and causing water disruptions for account users (Lee Goi, 2020b).…”

“…(Osmi et al, 2016) k. (Afroz et al, 2014) l. (Loi et al, 2022) m. (Che Osmi et al, 2018) n. (Adnan et al, 2022 o. (Khullar & Singh, 2021) p. (Shamsuddin et al, 2022) q. (Khalid et al, 2018) r. (Bartram & Pedley, 1996) s. (Viswanathan et al, 2010) t.…”

“…In WQI computation, several parameters are selected, sub-index values are generated, parameter weights are generated, and index aggregation techniques are selected, and no universally acceptable approach exists. A comparison of this final index with the WQI range determines the water quality class (Shamsuddin et al, 2022).…”

Water Quality Modeling and Monitoring

“…When water pollution is a severe issue, forecasting water quality to control water pollution and informing consumers in the event of the detection of poor water quality is crucial. Water quality index (WQI) classification models based on machine learning were developed for the Langat River Basin in Malaysia [15] to forecast the WQI as a way to enhance water quality management. These machine learning models include deep learning techniques such as multilayer perceptron (MLP) and long short-term memory (LSTM) networks in a multitask learning framework [13]; six supervised machine learning algorithms (e.g., logistic regression, support vector machines (SVM)) to classify biophysical conditions (clear, moderate, Chladominated, and turbid) for 357 lakes and ponds in Lithuania [14]; and artificial neural networks (ANNs), decision trees (DTs), and SVM to classify river water quality [15].…”