Interpretable Deep Learning for Characterizing Sinkhole to Supply Well Transfer Dynamics in Karst Aquifers

In karstic environments, water supply wells are vulnerable to rapid sediment transfer during intense rainfall events, often generating turbidity peaks that disrupt water-treatment operations. In Normandy (France), the high density of sinkholes and the complexity of transport processes in karsts complicate the identification and prioritization of sinkholes requiring mitigation to reduce sediment fluxes at water supply wells. This study aims to quantify the time-lagged impact of each sinkhole on turbidity peaks at a supply well using a cascade modeling approach that couples numerical surface erosion–runoff simulations with deep learning models representing hydrosedimentary responses through the karst network. Surface erosion–runoff was simulated using WaterSed. Hydroclimatic time series and WaterSed model outputs were used as inputs for our deep learning models. Several deep learning architectures were compared and optimized across multiple rounds to identify a best-performing model, which was then interpreted using interpretability methods. Interpretability analyses show that turbidity is primarily controlled by seasonal conditions and short-term rainfall accumulation, while multiple sinkholes contribute jointly to short time lags. Temporal attributions reveal rapid karst response followed by attenuation, consistent with reactive karst behavior. The contribution of each sinkhole to turbidity peaks allows us to identify the most important sinkholes requiring mitigation by stakeholders.