Perspectives on water quality analysis emphasizing indexing, modeling, and application of artificial intelligence for comparison and trend forecasting
Abstract Freshwater essential for civilization faces risk from untreated effluents discharged by industries, agriculture, urban areas, and other sources. Increasing demand and abstraction of freshwater deteriorate the pollution scenario more. Hence, water quality analysis (WQA) is an important task...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2025-05-01
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| Series: | River |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/rvr2.70012 |
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| Summary: | Abstract Freshwater essential for civilization faces risk from untreated effluents discharged by industries, agriculture, urban areas, and other sources. Increasing demand and abstraction of freshwater deteriorate the pollution scenario more. Hence, water quality analysis (WQA) is an important task for researchers and policymakers to maintain sustainability and public health. This study aims to gather and discuss the methods used for WQA by the researchers, focusing on their advantages and limitations. Simultaneously, this study compares different WQA methods, discussing their trends and future directions. Publications from the past decade on WQA are reviewed, and insights are explored to aggregate them in particular categories. Three major approaches, namely—water quality indexing, water quality modeling (WQM) and artificial intelligence‐based WQM, are recognized. Different methodologies adopted to execute these three approaches are presented in this study, which leads to formulate a comparative discussion. Using statistical operations and soft computing techniques have been done by researchers to combat the subjectivity error in indexing. To achieve better results, WQMs are being modified to incorporate the physical processes influencing water quality more robustly. The utilization of artificial intelligence was primarily restricted to conventional networks, but in the last 5 years, implications of deep learning have increased rapidly and exhibited good results with the hybridization of feature extracting and time series modeling. Overall, this study is a valuable resource for researchers dedicated to WQA. |
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| ISSN: | 2750-4867 |