軟DTWベースのクラスタリングとグラフニューラルネットワークフレームワークを用いた地下水重金属汚染の時空間予測

Accurate prediction of groundwater heavy metal contaminant spatiotemporal dynamics is essential for monitoring optimization and remediation decision-making at contaminated sites. However, heterogeneous contamination distribution and complex spatiotemporal correlations among monitoring wells pose significant prediction challenges. In this study, a Soft Dynamic Time Warping clustering-based Graph Neural Network (SDCGNN) was proposed for contamination zone identification, integrating multi-scale spatiotemporal modeling. Using Soft Dynamic Time Warping (Soft-DTW) distance-based clustering, the model partitions monitoring wells into source, plume, and attenuation zones according to temporal contamination patterns, while a hierarchical local-global graph fusion framework captures both zone-specific dynamics and cross-zone transport processes. Evaluation on two-year hourly monitoring data from 25 monitoring wells at on-site contaminated sites demonstrated that SDCGNN achieved average Mean Absolute Error (MAE) of 0.213 mg/L and Mean Absolute Percentage Error (MAPE) of 5.51%, improving upon baseline models by 49.9% and 61.4%, respectively. Zone-specific predictions revealed high accuracy across source, plume, and attenuation areas despite varying concentration ranges and spatial heterogeneity. Furthermore, spatiotemporal analysis confirmed that the model accurately reproduced observed contamination transport patterns, including plume migration directions and concentration gradient evolution. The proposed zone-aware modeling approach shows promise for advancing groundwater heavy metal contamination prediction capabilities and facilitates the optimization of site remediation strategies.