Water quality prediction based on sparse dataset using enhanced machine learning.

Sheng Huang, Jun Xia, Yueling Wang, Jiarui Lei, Gangsheng Wang

Abstract read

In one paragraph

Article in Environmental science and ecotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Augmented machine learning for sewage quality assessment with limited data.Environmental science and ecotechnology · 2025
Article

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sheng HuangState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China.

Jun XiaState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China.

Yueling WangKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

Jiarui LeiDepartment of Civil and Environmental Engineering, National University of Singapore, 117578 Singapore.

Gangsheng WangState Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for COD

Indexed as

Load estimatorLong short-term memoryMachine learningRiver-lake confluenceSparse measurementWater quality modeling

Identifiers

PMID38585199

DOI10.1016/j.ese.2024.100402

PMCPMC10998092

What OpenQuestion holds

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.