Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran.
Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Environ Monit Assess. 2018 Feb 17;190(3):149. doi: 10.1007/s10661-018-6507-8.
Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.
不断增长的对不同用途水资源的需求使得人们必须更好地了解和认识水资源。众所周知，地下水是主要水资源之一，特别是在干旱气候条件的国家。因此，本研究旨在利用新算法提供地下水潜力图（GPM）。为此，本研究旨在验证 C5.0、随机森林（RF）和多元自适应回归样条（MARS）算法在伊朗马什哈德平原东部生成 GPM 的性能。为此，生成了一个包含泉水位置作为指示物和地下水条件因素（GCF）作为输入的数据集。在本研究中，选择了 13 个 GCF，包括海拔、坡度方向、坡度角度、平面曲率、剖面曲率、地形湿度指数（TWI）、坡度长度、与河流和断层的距离、河流和断层密度、土地利用和岩性。所提到的数据集分为训练和验证两类，泉水分别占 70%和 30%。然后，使用 R 统计软件对 C5.0、RF 和 MARS 算法进行了应用，最终值被转换为 GPM。最后，计算了两个评估标准，包括 Kappa 和接收器操作特征曲线下的面积（AUC-ROC）。根据本研究的结果，MARS 的表现最佳，AUC-ROC 为 84.2%，其次是 RF 和 C5.0 算法，AUC-ROC 值分别为 79.7%和 77.3%。结果表明，所采用模型的 AUC-ROC 值均超过 70%，表明其具有可接受的性能。总之，所产生的方法可以在其他地理区域使用。GPM 可以供水资源管理者和相关组织使用，以加速和促进水资源开发。
