Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hydrological Forecasting Using AI · Water Quality Monitoring Technologies · Water Quality Monitoring and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China
Gansu Engineering Research Center for Eco-Environmental Intelligent Networking, Lanzhou 730030, China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Gansu Province · Fundamental Research Funds for the Central Universities · Higher Education Innovation Fund Project of Gansu Province
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