Deep Reinforcement Learning for Environmental Pollution Analysis and Source Identification in China

Environmental pollution is a major global issue, posing significant threats to human health and ecological balance. Environmental pollution analysis is an important tool for environmental pollution prevention and control, which mainly includes the prediction of the concentration and distribution of pollutants, as well as the source identification of the sources and contributions of pollutants. However, there are many limitations in the existing environmental pollution analysis methods, such as large computational volume, long time-consumption, low accuracy, and poor generalization. To address these problems, this paper proposes a deep reinforcement learning-based environmental pollution analysis method, which utilizes deep neural networks and reinforcement learning algorithms to automatically learn and optimize the strategies for environmental pollution analysis, and to achieve efficient, accurate and intelligent prediction and source identification of pollutants. In this paper, experiments are conducted on four different datasets, which are the air quality data of the whole country of China, the North China Plain, the Fenwei Plain, and the Sichuan-Chongqing Plain, the proposed method was compared with traditional numerical simulation methods and machine learning techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of prediction accuracy, source identification accuracy, computational efficiency, and generalization ability, thereby proving the effectiveness and innovation of deep reinforcement learning in environmental pollution analysis. This paper provides a new idea and tool for environmental pollution analysis, which is of great significance and value for environmental pollution prevention and sustainable development.