Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance...

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Vydáno v:Applied sciences Ročník 14; číslo 19; s. 8755
Hlavní autoři: Chen, Jixuan, Wei, Xiaojuan, Liu, Yinxiao, Zhao, Chunxia, Liu, Zhenan, Bao, Zhikang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2024
Témata:
Accuracy
Algorithms
Aquatic resources
Case studies
Chaos theory
China
Computational linguistics
Deep learning
Forecasts and trends
Industrial development
Language processing
Linear model
Machine learning
Management
Natural language interfaces
Neural networks
Pollution control
Rivers
Statistical methods
Surface water
System theory
Trends
Water
Water pollution
Water quality
water quality data
water quality prediction
Water resources
China
ISSN:2076-3417, 2076-3417
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Shrnutí:Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app14198755