Optimal Pollution Source Identification via machine learning approach based on X-T-C image recognition and Inpainting

Pollution Source Identification (PSI) based on watershed environmental sensing (IoT, low-cost sensors, etc.) is a key topic in hydroinformatics and watershed water resources/quality management, and timely, accurate PSI is crucial for reducing water environmental risks. Machine learning-based PSI directly maps water environmental observations to source information, offering high computational efficiency and emerging as a new research trend. However, the high uncertainty and spatial sparsity in water environmental observations force Machine learning-based PSI methods to face the trade-off problem between PSI accuracy and data volume-quality requirements, creating an urgent need for data-demand-reduction strategies to facilitate the PSI practical adoption in water management. Therefore, this study proposes an X-T-C image recognition-based ResNet Machine learning PSI method coupled with data Inpainting techniques (InRes-PSI). InRes-PSI converts spatial coordinates (X), time (T), and pollutant concentration (C) into 2D images and realizes end-to-end localization and reconstruction through multi-feature convolution, reducing the interference of data uncertainty; In addition, InRes-PSI integrates an image inpainting strategy to fill missing data under sparse monitoring conditions, thereby ensuring reliable PSI with fewer data, reducing the data volume demand of PSI. Tests on real and semi-synthetic river cases show that InRes-PSI effectively handles non-point pollution uncertainty interference, improving PSI accuracy by 6.27% and 7.72% compared to the Batch-Matching method and the LeNet, respectively; As for data-demand-reduction, the inpainting strategy enables reliable PSI even when half of the grid data are missing, which can reduce the density of stations by about 55% in a real watershed. Additionally, we discovered a logarithmic relationship between the river flow field characteristic (Péclet number) and sensor deployment density, indicating that diffusion-dominated rivers require higher sensor density. This finding can provide an intuitive and transferable design for water-environment sensing and digital watershed management.