Sensing and Reasoning of Water Quality Based on Deep Reinforcement Learning in Complex Watershed

Aquatic information monitoring is crucial for the sustainable management of water environments. Conventional interpolation methods commonly hinge on assumptions of spatial proximity or temporal similarity. However, they often fall short of capturing the intricate spatiotemporal correlations present in water quality sequences, affecting our understanding of the spatial patterns of regional water quality conditions. In this study, we propose a framework for river basin information fine-grained sensing based on deep learning, which includes a global sensing model (SGM) and a static deployment model. Inside the SGM, we adopt a multidimensional convolutional neural network (CNN) to extract spatiotemporal features and an attention mechanism to fuse these features, to infer water quality variable information on unmonitored points. Since the inference outcomes could be affected by the locations of the sensors, to minimize the inference error of the SGM, the static deployment model was designed to aid the deployment of sensors into strategic locations of a river basin to obtain optimum spatial-temporal data samples. The research results not only revealed the spatial distribution patterns of total nitrogen (TN) concentrations but also showed that the proposed method could yield a better inference performance compared to traditional interpolation methods.