Research on Watershed Water Quality Classification Prediction Based on WOA-CNN-GRU Model

\nRiver water quality monitoring plays a crucial role in water environment protection and management. This paper aims to explore and innovatively propose an efficient and accurate water quality classification prediction model WOA-CNN-GRU composite model. By ingeniously integrating WOA, GRU, and CNN, this model conducts comprehensive predictive research on the complex and dynamic water quality environments of the Yellow River basins in China. Addressing limitations of traditional water quality prediction methods such as single-model approaches, inadequate data processing capabilities, and limited prediction accuracy, the WOA-CNN-GRU model constructed in this study integrates core technologies from different algorithms to achieve comprehensive analysis and efficient handling of water quality data.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-10-23
DOI
https://doi.org/10.1051/e3sconf/202458001007/pdf
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on Watershed Water Quality Classification Prediction Based on WOA-CNN-GRU Model

Springer Link (Chiba Institute of Technology)
Mycorrhizal Fungi and Plant Interactions
article

Research on Watershed Water Quality Classification Prediction Based on WOA-CNN-GRU Model

article en

Abstract

\nRiver water quality monitoring plays a crucial role in water environment protection and management. This paper aims to explore and innovatively propose an efficient and accurate water quality classification prediction model WOA-CNN-GRU composite model. By ingeniously integrating WOA, GRU, and CNN, this model conducts comprehensive predictive research on the complex and dynamic water quality environments of the Yellow River basins in China. Addressing limitations of traditional water quality prediction methods such as single-model approaches, inadequate data processing capabilities, and limited prediction accuracy, the WOA-CNN-GRU model constructed in this study integrates core technologies from different algorithms to achieve comprehensive analysis and efficient handling of water quality data.\n

Springer Link (Chiba Institute of Technology)
National Science Foundation, National Natural Science Foundation of China, Education Department of Shaanxi Province, Natural Science Basic Research Program of Shaanxi Province
Clean water and sanitation
Openalex Percentile: Top 60%
Mycorrhizal Fungi and Plant Interactions
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