Optimizing Ecological Water Replenishment in Xianghai Wetlands Using CNN-LSTM and PSO Algorithm Under Secondary Salinization Constraints

Wetlands play a crucial role in water purification, climate regulation, and biodiversity conservation. However, the Xianghai wetlands (situated in Tongyu County, Jilin Province, China) have experienced severe ecological degradation due to natural factors and unsustainable human activities, leading to declining groundwater levels and intensified salinity issues. To address these problems, this study aims to optimize ecological water replenishment strategies for the Xianghai nature reserve by integrating groundwater numerical simulation, surrogate modeling (convolutional neural network–long short-term memory neural network, CNN-LSTM), and intelligent optimization algorithms (Particle Swarm Optimization, PSO). During the design of the water replenishment scheme, the objective function maximizes the replenishment volume while considering the secondary salinization of soil in the reserve and its surrounding areas as a constraint. The results show that the surrogate model established using the convolutional neural network–long short-term memory neural network achieved high accuracy, with R2 values of 0.9996 and 0.9962 and MREs of 0.0023 and 0.0089 for training and validation sets, respectively; Compared to the random replenishment scheme, the optimized water replenishment scheme significantly reduces secondary salinization. After 10 years water replenishment, the optimized scheme exhibited a 2 km2 reduction in the salinized area compared to the randomized scheme, with the degree of salinization being reduced from moderate to mild. This method improves ecological sustainability and can be adapted to meet local water use demands. This simulation-optimization method provides an effective approach for designing water replenishment schemes that address secondary salinization.