Mussa Mussa Muhaba, Lohani Tarun Kumar, Eshete Abunu Atlabachew
Faculty of Water Resources and Irrigation Engineering Water Technology Institute Arba Minch University Arba Minch P.O. Box 21 Ethiopia.
Faculty of Hydraulic and Water Resources Engineering Water Technology Institute Arba Minch University Arba Minch P.O. Box 21 Ethiopia.
Glob Chall. 2024 Oct 16;8(12):2400137. doi: 10.1002/gch2.202400137. eCollection 2024 Dec.
The main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) and Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, and lineament density. This is combined with datasets used for training and validating the RF and SVM models, which consisted of 75 potential sites (boreholes and springs), 22 non-potential sites (bare lands and settlement areas), and 20 potential sites (water bodies). Each dataset is randomly partitioned into two sets: training (70%) and validation (30%). The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC-ROC). The AUC of the RF model is 0.91, compared to 0.88 for the SVM model. Both models classified GWPZs effectively, but the RF model performed slightly better. The classified GWPZ map shows that high GWPZs are typically located within water bodies, natural springs, low-lying regions, and forested areas. In contrast, low GWPZs are primarily found in shrubland and grassland areas. This study is vital for decision-makers as it promotes sustainable groundwater use and ensures water security in the studied area.
本研究的主要目的是使用先进的集成机器学习（ML）模型，特别是随机森林（RF）和支持向量机（SVM），来绘制和评估地下水潜力区（GWPZs）。通过考虑地质、排水密度、坡度、土地利用/土地覆盖（LULC）、降雨、土壤和线性构造密度等关键因素来识别GWPZs。这与用于训练和验证RF和SVM模型的数据集相结合，该数据集包括75个潜在地点（钻孔和泉水）、22个非潜在地点（裸地和居民区）以及20个潜在地点（水体）。每个数据集被随机分为两组：训练集（70%）和验证集（30%）。使用接收器操作特征曲线下的面积（AUC-ROC）来评估模型的性能。RF模型的AUC为0.91，而SVM模型的AUC为0.88。两个模型都有效地对GWPZs进行了分类，但RF模型的表现略好。分类后的GWPZ地图显示，高GWPZs通常位于水体、天然泉水、低洼地区和森林地区。相比之下，低GWPZs主要出现在灌丛地和草地地区。本研究对决策者至关重要，因为它促进了地下水的可持续利用，并确保了研究区域的水安全。
