当前位置: X-MOL 学术GISci. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Application of machine learning techniques in groundwater potential mapping along the west coast of India
GIScience & Remote Sensing ( IF 6.7 ) Pub Date : 2020-07-20 , DOI: 10.1080/15481603.2020.1794104
Pankaj Prasad 1, 2 , Victor Joseph Loveson 1, 3 , Mahender Kotha 2 , Ramanand Yadav 1, 3
Affiliation  

ABSTRACT Groundwater potential mapping (GWPM) in the coastal zone is crucial for the planning and development of society and the environment. The current study is aimed to map the groundwater potential zones of Sindhudurg coastal stretch on the west coast of India, using three machine learning models: random forest (RF), boosted regression tree (BRT), and the ensemble of RF and support vector machine (SVM). In order to achieve the objective, 15 groundwater influencing factors including elevation, slope, aspect, slope length (LS), profile curvature, plan curvature, topographical wetness index (TWI), distance from streams, distance from lineaments, lithology, geomorphology, soil, land use, normalized difference vegetation index (NDVI), and rainfall were considered for inter-thematic correlations and overlaid with spring and well occurrences in a spatial database. A total of 165 spring and well locations were identified, which had been divided into two classes: training and validation, at the ratio of 70:30, respectively. The RF, BRT, and RF-SVM ensemble models have been applied to delineate the groundwater potential zones and categorized into five classes, namely very high, high, moderate, low, and very low. RF, BRT, and ensemble model results showed that 33.3%, 35.6%, and 36.8% of the research area had a very high groundwater potential zone. These models were validated with area under the receiver operating characteristics (AUROC) curve. The accuracy of RF (94%) and hybrid model (93.4%) was more efficient than BRT (89.8%) model. In order to further evaluate and validate, four different sites were subsequently chosen, and we obtained similar results, ensuring the validity of the applied models. Additionally, ground-penetrating radar (GPR) technique was applied to predict the groundwater table and validated by measured wells. The mean difference between measured and GPR predicted groundwater table was 14 cm, which reflected the importance of GPR to guide the location of new wells in the study region. The outcomes of the study will help the decision-makers, government agencies, and private sectors for sustainable planning of groundwater in the area. Overall, the present study provides a comprehensive high-precision machine learning and GPR-based groundwater potential mapping.

中文翻译:

机器学习技术在印度西海岸地下水潜力测绘中的应用

摘要 沿海地区的地下水潜力测绘(GWPM)对于社会和环境的规划和发展至关重要。目前的研究旨在使用三种机器学习模型绘制印度西海岸 Sindhudurg 海岸带的地下水潜力区:随机森林 (RF)、增强回归树 (BRT) 以及 RF 和支持向量机的集合(支持向量机)。为实现这一目标,15个地下水影响因素包括高程、坡度、坡向、坡长(LS)、剖面曲率、平面曲率、地形湿度指数(TWI)、与河流的距离、与线条的距离、岩性、地貌、土壤, 土地利用, 归一化差异植被指数 (NDVI), 和降雨量被考虑用于主题间相关性,并与空间数据库中的春季和井发生重叠。共识别出165个弹簧和井位,按70:30的比例分为训练和验证两类。RF、BRT 和 RF-SVM 集成模型已被应用于描绘地下水潜力区,并将其分为五类,即极高、高、中、低和极低。RF、BRT 和集合模型结果表明,33.3%、35.6% 和 36.8% 的研究区域具有非常高的地下水潜力区。这些模型通过接受者操作特征 (AUROC) 曲线下的面积进行了验证。RF (94%) 和混合模型 (93.4%) 的准确率比 BRT (89.8%) 模型更有效。为了进一步评估和验证,随后选择了四个不同的站点,我们得到了相似的结果,确保了应用模型的有效性。此外,还应用探地雷达(GPR)技术预测地下水位并通过实测井进行验证。测得的和 GPR 预测的地下水位之间的平均差异为 14 cm,这反映了 GPR 在指导研究区域新井位置的重要性。研究结果将帮助决策者、政府机构和私营部门对该地区地下水进行可持续规划。总体而言,本研究提供了全面的高精度机器学习和基于 GPR 的地下水潜力绘图。应用探地雷达(GPR)技术预测地下水位并通过实测井进行验证。测得的和 GPR 预测的地下水位之间的平均差异为 14 cm,这反映了 GPR 在指导研究区域新井位置的重要性。研究结果将帮助决策者、政府机构和私营部门对该地区地下水进行可持续规划。总体而言,本研究提供了全面的高精度机器学习和基于 GPR 的地下水潜力绘图。应用探地雷达(GPR)技术预测地下水位并通过实测井进行验证。测得的和 GPR 预测的地下水位之间的平均差异为 14 cm,这反映了 GPR 在指导研究区域新井位置的重要性。研究结果将帮助决策者、政府机构和私营部门对该地区地下水进行可持续规划。总体而言,本研究提供了全面的高精度机器学习和基于 GPR 的地下水潜力绘图。这反映了 GPR 在指导研究区域新井位置方面的重要性。研究结果将帮助决策者、政府机构和私营部门对该地区地下水进行可持续规划。总体而言,本研究提供了全面的高精度机器学习和基于 GPR 的地下水潜力绘图。这反映了 GPR 在指导研究区域新井位置的重要性。研究结果将帮助决策者、政府机构和私营部门对该地区地下水进行可持续规划。总体而言,本研究提供了全面的高精度机器学习和基于 GPR 的地下水潜力绘图。
更新日期:2020-07-20
down
wechat
bug