Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, 19697, Iran.
Chemosphere. 2024 Sep;363:142859. doi: 10.1016/j.chemosphere.2024.142859. Epub 2024 Jul 20.
Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AUC values of 0.813 and 0.810, respectively, indicating comparable performance. The outcomes of this study provide valuable insights for policymakers, offering a robust framework for tackling water scarcity challenges in arid regions through precise and reliable groundwater potential assessments.
解决干旱地区的水资源短缺问题是当务之急，需要创新的解决方案来进行准确的地下水潜力测绘（GPM）。本研究全面评估了先进的建模技术，以提高 GPM 的精度。本研究在伊朗的赞德鲁德流域进行，使用了一个包含 16 个地下水潜力影响因素的空间数据库和 175 口井的数据。本研究通过增强随机森林（RF）算法，引入了一种地下水潜力测绘的创新方法。这种增强涉及到集成三种受人类行为启发的元启发式算法：ICA（帝国主义竞争算法）、TLBO（基于教学的优化）和 SBO（基于学生心理学的优化）。建模过程使用了 70%的训练数据和 30%的评估数据。数据预处理采用共线性测试方法和频率比（FR）技术对数据集进行优化。随后，使用四种不同的模型生成地下水潜力图，展示了机器学习和受人类启发的元启发式算法的综合能力。通过广泛的统计分析，系统地评估了模型的性能，包括均方根误差（RMSE）、平均绝对误差（MAE）、曲线下面积（AUC）用于接收者操作特征曲线（ROC）的弗里德曼检验、卡方检验和 Wilcoxon 符号秩检验。RF-ICA 和 RF-SPBO 表现出色，具有统计学上可比的精度，明显优于 RF-TLBO 和非优化的 RF 模型。地下水潜力图的结果显示，RF-ICA 的准确性非常高，其 AUC 得分高达 0.865，突出了其在区分不同地下水潜力类别的优越性。RF-SPBO 的 AUC 也达到了 0.842，表明其在不准确分类方面的有效性。RF-TLBO 和非优化的 RF 模型的 AUC 值分别为 0.813 和 0.810，表明性能相当。本研究的结果为决策者提供了有价值的见解，为通过精确和可靠的地下水潜力评估来应对干旱地区的水资源短缺挑战提供了一个强大的框架。
