An investigation is presented in this paper to predict Groundwater Level (GWL) by Inclusive Multiple Modelling (IMM) practices, introduced recently by the authors, as a strategy by organising multiple models at three hierarchical levels: Artificial Neural Networks (ANNs), Sugeno Fuzzy Logic (SFL), Neuro-Fuzzy (NF), Support Vector Machine (SVM) and Gene Expression Programming (GEP). The IMM novelty of the study is to investigate a modelling strategy at three hierarchical levels, such that any base models at Level 1 are not reused as the combiner model at Levels 2 and Level 3 and this leads to a number of strategies. The results provide some evidence that (i) combining base models at Levels 2 and 3 enhance the performances compared with those of individual base models at Level 1; and (ii) the results at Level 3 become defensible and thereby suitable for the development of management scenarios. The decline in GWLs is investigated through management scenarios, which show that water use has higher impacts on groundwater level variations in the study area than those by climatic variabilities and this underpins the evidence for the necessity of management plans and strategies for the study area.

本文提出了一项调查，旨在通过作者最近介绍的“包容性多种建模（IMM）”实践来预测地下水位（GWL），以此作为在三个层次级别组织多个模型的策略：人工神经网络（ANN），Sugeno Fuzzy逻辑（SFL），神经模糊（NF），支持向量机（SVM）和基因表达编程（GEP）。该研究的IMM新颖性在于研究三个层次级别的建模策略，以使第1层的任何基础模型都不能作为第2层和第3层的组合器模型重复使用，这导致了许多策略。结果提供了一些证据，（i）与第1级的单个基础模型相比，在第2级和第3级的基础模型组合起来可以提高性能；（ii）第3级的结果具有说服力，因此适合开发管理方案。通过管理情景调查了全球总产值的下降，这表明用水对研究区地下水位变化的影响要大于气候变化对地下水位变化的影响，这为研究区管理计划和策略的必要性提供了依据。