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Integration of interval rough AHP and fuzzy logic for assessment of flood prone areas at the regional scale

机译:区间粗糙AHP与模糊逻辑的整合,以评估区域规模洪水易发区域

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This study was conducted to prepare a flood susceptibility map in northwest of Hamadan Province, Iran. For this purpose, six criteria related to flood (i.e., distance to discharge channel, slope (%), elevation, soil texture and land use, topographic wet index, and check dams) were chosen. Then, based on the role of these criteria on degree of flood susceptibility, were weighted both in the context of inter-weighting (fuzzy logic) and outer-criteria (Interval Rough Analytic Hierarchy Process). Finally, by combining these primary weights by weight overlay method in GIS, the flood susceptibility mapping was prepared in the study area. The resulted map based on K-means clustering and Silhouette function was divided into 9 clusters, whereas the lower clusters show low susceptibility to flood and vice versa. To assess the accuracy of the produced map, 102 flood observation points were overlaid on the clustered flood susceptibility map. The results showed that among these 102 flood points, 66 points are located in the clusters 8 and 9 and 3 points are located on cluster 7. These values show that the produced flood susceptibility mapping has a high accuracy.
机译:本研究进行了伊朗哈马丹省西北部的洪水敏感性图。为此目的,选择了与洪水有关的六个标准(即到放电通道,斜坡(%),高程,土地纹理和土地使用,地形湿指数和校验坝)。然后,根据这些标准对洪水敏感度程度的作用,在重加权(模糊逻辑)和外部标准(间隔粗略分析层次处理)的上下文中加权。最后,通过在GIS中将这些初级重量组合在GIS中,在研究区域中制备了洪水敏感性映射。基于K-Means聚类和轮廓函数的所产生的地图被分成9个集群,而下集群显示出对洪水的低敏感性,反之亦然。为了评估所产生的地图的准确性,102次洪水观察点覆盖在聚类洪水敏感性图上。结果表明,在这102个泛光点中,66个点位于集群8和9中,在群集7个点处。这些值表明,所产生的洪水敏感性映射具有高精度。

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