The main goal of the present study is to develop hydrological security model (HSM) and landscape insecurity model (LIM) of the wetlands in moribund deltaic floodplain using a tree-based hybrid ensemble method. The study employs four tree-based novel hybrid approaches such as Random Forest (RF), Extremely randomized forest (ETC), gradient boosting (GBM), and eXtreme gradient boosting (XGB) for modelling hydrological security and landscape insecurity. Six hydrological parameters such as water presence frequency (WPF), water depth, Hydro-duration, variability of water depth using standard deviation, distance from rivers, and regression slope of wetland depth have been employed for hydrological security modelling, and nine landscape parameters such as aggregation index, patch cohesion index, edge density, mean radius of gyration arithmetic, largest patch Index, mean perimeter-area ratio, percentage of landscape, splitting index, total edge have been employed for landscape insecurity modelling. The performance of each model is evaluated by estimating precision, recall, F1-score, Matthew's correlation coefficient (MCC), and the area under the receiver operating characteristic (ROC) curve (AUC). The outcomes revealed that GBM and XGB pose the highest accuracy level (AUC more than 0.95 for HSM and 0.85 for LIM), followed by RF, ETC models. Models' outcome shows that about 50% of wetland area belongs to the low hydrological secure zone. From phase I to phase III this area increased by more than 18%. The area under high hydrological secure zones reduces by about 55%. Landscape insecurity in this region raised by 41% from phase I to phase III. Linking HSM and LIM shows that reduction of hydrological security is responsible for enhancing landscape insecurity in this region.

本研究的主要目标是使用基于树的混合集成方法开发垂死的三角洲洪泛区湿地的水文安全模型 (HSM) 和景观不安全模型 (LIM)。该研究采用四种基于树的新型混合方法，如随机森林 (RF)、极端随机森林 (ETC)、梯度提升 (GBM) 和极限梯度提升 (XGB)，用于对水文安全和景观不安全进行建模。水文安全建模采用了水存在频率（WPF）、水深、水文持续时间、使用标准差的水深变异性、与河流的距离和湿地深度的回归斜率等六个水文参数，以及九个景观参数，例如作为聚集指数，斑块内聚指数，边缘密度，平均回转半径算法，最大斑块指数、平均周长面积比、景观百分比、分裂指数、总边缘已被用于景观不安全建模。每个模型的性能通过估计精度、召回率、F1 分数、马修相关系数 (MCC) 和受试者工作特征 (ROC) 曲线下面积 (AUC) 来评估。结果表明，GBM 和 XGB 的准确度水平最高（HSM 的 AUC 超过 0.95，LIM 的 AUC 超过 0.85），其次是 RF、ETC 模型。模型结果表明，大约 50% 的湿地面积属于低水文安全区。从第一阶段到第三阶段，这个面积增加了 18% 以上。高水文安全区面积减少约55%。从第一阶段到第三阶段，该地区的景观不安全感增加了 41%。