Enhanced Water Quality Inversion in the Ningxia Yellow River Basin Using a Hybrid PCWA-ResCNN Model: Insights from Landsat-8 Data
Source abstract
Abstract
The real-time monitoring and evaluation of water quality provides a scientific basis for water resource management and promotes regional sustainable development. This study established a database using Landsat-8 satellite data and water quality data from the Ningxia Yellow River basin in China, spanning 2021 to 2023, and this paper proposes a custom residual convolutional neural network model with a hybrid attention mechanism, referred to as PCWA-ResCNN. The accuracy of the model in predicting turbidity, permanganate, ammonia nitrogen, and dissolved oxygen concentration was more than 95%. Compared to convolutional neural networks and long short-term memory models, this model performed better in predicting water quality parameters with significantly improved prediction performance. In terms of spatial distribution, the pollution degree in the middle reaches of the basin is relatively serious. However, the overall water quality is good, being mainly Class I and Class II water quality. The hybrid model established in this paper can better capture the complex nonlinear relationship between the observed values and the surface water reflectance, showing strong robustness. This model can be used for the water quality monitoring of complex inland rivers and lakes, and it can also provide effective support for relevant government departments to formulate scientific and reasonable water quality management policies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Marine and coastal ecosystems · Hydrology and Watershed Management Studies · Environmental Changes in China
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics and Electrical Engineering, Ningxia University, Yinchuan 750021, China
Key Laboratory of Desert Information Intelligent Perception, Ningxia University, Yinchuan 750021, China
Funding
National Natural Science Foundation of China · Central Government supported the Local Special Fund Project of China
References (42)
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