A Multi-Objective Optimization Framework for Check Dam Siting Integrating GIS, a Hydrological–Hydrodynamic Coupling Model, and NSGA-II

As a key soil and water conservation measure, check dams play an important role in erosion control, flood mitigation, and ecological restoration. Their scientific siting is the core prerequisite for realizing these composite benefits. To address the limitations of traditional siting methods, which are characterized by strong subjectivity, low efficiency, and incomplete multi-objective collaborative optimization, a multi-objective optimization framework integrating GIS, hydrological–hydrodynamic coupled models, and a genetic algorithm is proposed in this study. By parameterizing the distance from the watershed outlet (S) and the dam height (H), a continuous decision space is constructed, establishing quantitative mapping relationships with the dam crest length, sediment storage capacity, and silted land area. By coupling the HEC-HMS and HEC-RAS models, a flood surrogate model is developed to dynamically predict the peak flood reduction benefits under a 100-year flood scenario. Based on the Nondominated Sorting Genetic Algorithm II, a multi-objective optimization model incorporating the construction cost (C), peak flow attenuation (P), and sediment retention and farmland creation (SRFC) benefits (E) is constructed, revealing the nonlinear regulatory mechanisms of the decision variables on the objectives. A case study demonstrates that the optimal solution set significantly clusters in the middle and lower reaches of the Yangjiagou watershed (S < 4.2 km). High dams located near the outlet (S < 2.2 km and H > 20 m) correspond to schemes with strong flood-control performance (P > 60%) . Schemes with advantageous benefit–cost ratios (E/C > 2.5) are distributed in the middle reaches (S > 3.2 km) and are characterized by low-dam systems (H < 17 m). This framework overcomes the spatial discretization and empirical dependence limitations of traditional check dam siting methods. It achieves prediction errors below 20%, providing a scientific tool that combines mechanistic interpretability and decision-making efficiency for check dam planning. The proposed framework further demonstrates engineering feasibility and transferability to other watersheds, offering practical value for soil and water conservation planning.