A review of the application of machine learning in water quality evaluation
importance 7/10
ai assisted
Hydrological Forecasting Using AI water management stage: research
Problem
Evaluating and managing water quality across surface water, groundwater, drinking water, sewage, and seawater using growing environmental data.
Why it matters
Water quality underpins public health, pollution control, and watershed ecosystem security.
What the AI did
Machine learning models perform data analysis, classification, and prediction for water quality evaluation, treatment, monitoring, and optimization systems.
Results
not stated in source
Impact
ML is described as an important tool applied across water treatment and management systems.
Record
| Venue | Eco-Environment & Health |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Mengyuan Zhu, Jiawei Wang, Xiao Yang, Yu Zhang, Linyu Zhang, Hongqiang Ren, Bing Wu, Lin Ye |
| Citations | 793 (OpenAlex, at import time) |
| References | 119 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1016/j.eehl.2022.06.001 |
| Open access | gold |
| Keywords | machine learning, water quality, prediction, monitoring, watershed management |
Importance rating
7
significant — thousands to millions affected
ML supports water pollution control, water quality improvement, and watershed ecosystem security across surface, ground, drinking, sewage, and seawater—broad public-health relevance; no scale stated.
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