Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

Abstract

Multidimensional indicators of surface water are key to assessing the water quality. Cost and time could be saved if surface water can be accurately assessed by fewer indicators. Therefore, it is necessary to screen key water quality indicators for different basins. This study collected 35 water quality indicators (42 315 observations) along the Wujingang basin. Cluster analysis and correlation coefficients were used to identify homogeneous categories of water quality indicators. Frequent pattern mining (FPM) was used to remove redundant indicators. Finally, the water quality assessment after the removal of redundant indicators was validated by classification analysis. Results of the silhouette coefficient and within-cluster sum of squared errors indicated that K-means was the optimal clustering model. Concomitant indicators Pb, Cl − , NO 3− , TN, V, and Al were identified using FPM. Decision tree verified that the performance did not decrease after removing Pb, Cl − , V, NO 3 − −N, and Al. These indicators were redundant for the Wujingang basin and could be monitored less frequently when there is no special use. This study provides important information for developing a selection framework based on multidimensional water quality data, which could serve as a baseline system for the selection of key water quality indicators in a specific basin.

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

“…The special issue includes several review articles encompassing a wide spectrum, ranging from a historical perspective of water data to computational modeling in wastewater treatment to ML modeling of environmental chemical reactions, environmental toxicology, heavy metal removal, and cyanobacterial harmful algal blooms (HABs) . One significant application of these innovative tools is ML-assisted environmental monitoring, which can address diverse problems, such as predicting effluent nutrients or influent flow rates and nutrient loads at wastewater treatment plants, , formation of disinfection byproducts, drivers of the accumulation of potentially toxic elements in sediments, greenhouse gas emissions, , occurrence of PFAS, water quality assessment, microplastics, microcystins, and differentiation of landfill leachate and domestic sludge . ML has also been extensively employed to model environmental chemical reactions and processes, including adsorption onto various materials, , biodegradation, photodegradation, and the physicochemical and meteorological variables that affect the seasonal growth and decline of HABs .…”