Eichhornia crassipes is one of the most ubiquitous invasive aquatic species in the world that negatively impact on native fauna and flora. The information on the occurrence of such invasive plant is crucial for the river/wetland management. The aim of the present study is to apply classification tree model (testing with the highest level of pruning confidence factor) integrated with an optimizer technique (greedy stepwise search algorithm) to predict the occurrence of this exotic species based on water quality and physical-habitat parameters. The ten sites (in the Anzali wetland, located in northern Iran) were monthly measured where the exotic species was present in 50 % of sampling sites and it was absent in the remaining of the sites. In total, 120 samples of E. crassipes (60 presence and 60 absence instances) were monthly measured together with 12 environmental variables during 1-year study period (2017–2018). For the model, two-third of datasets (80 instances) was employed for the training and the remainder for the validation set (40 instances). Before model optimizing (with the pruning confidence factor, PCF = 0.01), six variables were predicted by the model in three folds confirming that the non-occurrence of the exotic species might be associated with increasing flow velocity, depth of ecosystem, water turbidity, bicarbonate and dissolved oxygen concentration, while the occurrence of the exotic species (in terms of the abundance) might show an increase with increasing the concentration of nutrients such as phosphate. After model optimizing (with PCF = 0.01), the model selected five variables in three folds (flow velocity, depth, phosphate, bicarbonate, and nitrate) so that except nitrate, other selected variables were in common before and after variable selection. CT integrated with GS model thus proved to have a high potential when applied for decision-making in the context of wetland management.

凤眼莲（Eichhornia crassipes）是世界上最普遍的入侵水生物种之一，对本土动植物造成负面影响。有关这种入侵植物的发生的信息对于河流/湿地管理至关重要。本研究的目的是应用分类树模型（以最高水平的修剪置信度进行测试）与优化技术（贪婪逐步搜索算法）相结合，根据水质和自然栖息地预测该外来物种的发生参数。每月测量十个地点（位于伊朗北部的安扎利湿地），其中外来物种存在于50％的采样地点中，而其余地点则不存在。总共120份E. crassipes样本在为期1年的研究阶段（2017-2018年），每月（60个有事例和60个无事例）与12个环境变量一起进行测量。对于模型，训练使用了三分之二的数据集（80个实例），而验证集则使用了其余的数据集（40个实例）。在模型优化之前（采用修剪置信度因子，PCF = 0.01），模型预测了三个变量的六个变量，这证实了不存在外来物种可能与流速增加，生态系统深度，水浊度，碳酸氢盐和溶解氧的浓度，而外来物种的发生（以丰度计）可能随着营养物（例如磷酸盐）浓度的增加而增加。经过模型优化（PCF = 0.01），该模型以三倍选择了五个变量（流速，深度，磷酸盐，碳酸氢盐和硝酸盐），因此除了硝酸盐外，其他选择的变量在变量选择之前和之后都是相同的。因此，结合GS模型的CT证明在湿地管理中用于决策时具有很大的潜力。