Groundwater scarcity is one of the most concerning issues in arid and semi-arid regions. In this study, we develop and validate a novel artificial intelligence that is a coupling of five ensemble benchmark algorithms e.g., artificial neural network (ANN), reduced-error pruning trees (REPTree), radial basis function (RBF), M5P and random forest (RF) with particle swarm optimization (PSO) for delineating GWP zones. Further, nine parameters used for the GWP modelling and to test and train the proposed PSO-based models. Additionally, this study proposes a receiver operating characteristic (ROC) based sensitivity analysis for GWP modelling. Multicollinearity test, information gain ratio, and correlation attribute evaluation methods used to choose important parameters for the proposed GWP model. The result shows that drainage density, elevation, and land use/land cover have a higher influence on the GWP using correlation attribute evaluation methods. Results showed that the hybrid PSO-RF model performed better than other proposed hybrid models.

在干旱和半干旱地区，地下水稀缺是最令人担忧的问题之一。在这项研究中，我们开发并验证了一种新颖的人工智能，它结合了五个整体基准算法，例如，人工神经网络（ANN），减少错误的修剪树（REPTree），径向基函数（RBF），M5P和随机森林（RF）和粒子群优化（PSO）来描绘GWP区域。此外，九个参数用于GWP建模以及测试和训练建议的基于PSO的模型。此外，本研究提出了基于接收器工作特性（ROC）的GWP建模灵敏度分析。多重共线性测试，信息增益比和相关属性评估方法用于为建议的GWP模型选择重要参数。结果表明，排水密度，高程，使用相关属性评估方法，土地利用/土地覆盖对全球升温潜能值有较大影响。结果表明，混合PSO-RF模型的性能优于其他提出的混合模型。