Gas leakage
With the rapid pace of global urbanization and rising energy demands, efficient gas leak detection is vital for public safety. This study proposes an efficient and sensitive gas leak detection method based on reinforcement learning to enhance localization speed and robustness. The approach includes critical area identification, reinforcement learning model training, and leak point localization. Simultaneously introducing noise and missing data to test the robustness of the model.
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This dataset contains data collected from multiple paths, such as Unequally space path, Curved path, ESPLB, data collected from actual paths, and concentration prediction data. This experiment adopts a new concentration data collection path ESBLP method efficiently divides the study area into three parts and measures concentration data along the boundaries to calculate gradients.
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