使用遥感和基于GIS的模糊层次分析法(F-AHP)评估坦桑尼亚多多马Mpwapwa区的地下水潜力区

Ally Mgelwa Ally , Jianguo Yan , George Bennett , Neema Nicodemus Lyimo , Selassie David Mayunga
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引用次数: 0

摘要

地下水是社会经济发展的重要资源。潜在地下水资源位置的不确定性往往会导致一些地下水开发项目失败。水资源开发项目在大量资源投资后遭遇枯井是很常见的。在Mpwapwa区,钻孔位置是不确定的,通过基于地球物理调查方法的试错技术来确定,该方法涉及研究特定地质位置的岩石和土壤类型的行为。为了减少这种不确定性,本研究使用遥感和基于GIS的模糊层次分析法(F-AHP)来模拟坦桑尼亚多多马地区Mpwapwa区的地下水潜力区(GWPZ)。F-AHP模型用于对各种专题地图进行重新分类、加权和排序,包括岩性、土壤类型、排水密度、线性结构、磁场强度、坡度和高程。整个GWPZ地图是通过在GIS环境中结合七(7)个排名主题地图层创建的。然后使用两种方法验证得到的GWPZ图:叠加法和曲线下面积(AUC)法。由此产生的GWPZ地图显示,该地区分别有19%、31%、28%和22%被划分为非常好、良好、中等、差和非常差地区。使用叠加方法生成的映射的准确率为72%,使用AUC方法生成的地图的准确度为93%。
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Assessment of groundwater potential zones using remote sensing and GIS-based fuzzy analytical hierarchy process (F-AHP) in Mpwapwa District, Dodoma, Tanzania

Groundwater is a very important resource for socio-economic development. The uncertainty of where potential groundwater resources is located often causes some groundwater development projects to fail. It is common for water resources development projects hitting dry wells after heavy investments of resources. In Mpwapwa District, borehole drilling locations are uncertain, determined by trial-and-error techniques based on geophysical survey methods that involve the study of the behaviour of rock and soil types in specific geological locations. To reduce such uncertainty, this study used remote sensing and GIS-based Fuzzy Analytical Hierarchical Process (F-AHP) to simulate groundwater potential zones (GWPZ) in Mpwapwa District, Dodoma region, Tanzania. The F-AHP model was used to reclassify, weight, and rank various thematic maps, including lithology, soil types, drainage density, lineament, magnetic intensity, slope and elevation. The overall GWPZ map was created by combining the seven (7) ranking thematic map layers in a GIS environment. The resulting GWPZ map that was then validated using two methods: overlaying method and area under the curve (AUC) method. The resulting GWPZ map shows that 19%, 31%, 28% and 22% of the area are classified as very good, good, moderate, poor and very poor zones, respectively. The accuracy of the generated map is 72% using the overlaying method and 93% using the AUC method.

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