###### 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. L +
Footnote †: journal: selected topics  
reduce monitoring costs, and provide scientific support for water resource management and environmental protection. In recent years, the concentration estimation of water quality parameters combined with artificial intelligence technology has been extensively utilized in the field of water quality monitoring. [PERSON] et al. [7] used the LSTM model to conduct a detailed analysis of dissolved oxygen, electrical conductivity, and turbidity in terms of the water quality of the Yangtze River. [PERSON] et al. [8] established the point-center regression convolutional neural network (PSRCNN) of Sentinel-2 and Landsat-8 images to evaluate water transparency and achieved satisfactory results. [PERSON] et al. [9] established the inversion model of water quality parameters of inland reservoirs by using a BP neural network, examined water quality parameters such as chemical oxygen demand (COD) and permanganate (CODMn), and evaluated the eutrophication of reservoirs. The W-WaveNet multi-site water pollution prediction method proposed by [PERSON] et al. [10] combines adaptive graph convolution, a convolutional neural network, and a long short-term memory network, showing stable performance in predicting long series. [PERSON] et al. [11] proposed a deep learning model, FTGCN, based on a graph convolutional network to realize the prediction of multivariate water quality data. [PERSON] et al. [12] developed a convolutional neural network WaterNet model for Chl-a concentration retrieval, which is superior to the artificial neural network (ANN) model. In addition, [PERSON] et al. [13; 14] used spectral index, biooptical simulation, machine learning, and cloud computing techniques to monitor inland water quality, including dissolved oxygen (DO) and turbidity (TUB), and they analyzed the potential and limitations of these methods. [PERSON] et al. [15; 16; 17; 18] used convolutional neural networks and different remote sensing satellite data to invert Chl-a water quality parameters. [PERSON] et al. [19] predicted the water quality of ammonia nitrogen (NH\({}_{3}\)-N) and permanganate (CODMn) in Dongping Lake of the Yellow River in China based on the convlstm model, with it showing satisfactory performance. [PERSON] et al. [20] carried out research combining machine learning and RGB sensors to quantify and classify water turbidity (TUB), showing their application potential. [PERSON] et al.