The current study aimed to investigate the vulnerability state of wetland habitat as a result of damming. Wetland habitat vulnerability state (WHVS) models for pre and post-dam periods were built to investigate the impact, and the difference was assessed. Sixteen hydrological, land composition and water quality parameters were used for modelling WHVS. Swarm intelligence optimised machine learning algorithms such as SVM (Support Vector Machine), ANN (Artificial Neural Network), bagging, radial basis (RBF) and M5P model tree were developed. The models' efficiency was evaluated using statistical methods such as the Receiver operating characteristics (ROC) curve. According to the machine learning models, 8.13–14.58% of the area in the wetland fringe area, small patches, and edges was under the very high vulnerable wetland habitat status in the pre-dam period. During the post-dam period, the region covered by fringes and small and medium-core wetlands increased to 21.23–50.58%. The PSO-RBF model was found to be the best representative model. This study provides a large database of wetland habitat conditions, which could aid policymakers in developing wetland conservation and restoration plans.

目前的研究旨在调查由于筑坝造成的湿地栖息地的脆弱性状态。建立了坝前和坝后时期的湿地栖息地脆弱性状态 (WHVS) 模型以研究影响，并评估差异。16 个水文、土地成分和水质参数用于模拟 WHVS。开发了 SVM（支持向量机）、ANN（人工神经网络）、bagging、径向基（RBF）和 M5P 模型树等群体智能优化机器学习算法。使用统计方法评估模型的效率，例如接收者操作特征 (ROC) 曲线。根据机器学习模型，湿地边缘区 8.13-14.58% 的面积，小斑块，和边缘在建坝前处于非常脆弱的湿地栖息地状态。坝后时期，边缘和中小核心湿地覆盖面积增加到21.23-50.58%。发现 PSO-RBF 模型是最好的代表性模型。这项研究提供了一个大型湿地栖息地条件数据库，可以帮助决策者制定湿地保护和恢复计划。