首页> 外文期刊>Journal of Geographic Information System >Effectiveness of Fuzzy Overlay Function for Multi-Criteria Spatial Modeling—A Case Study on Preparation of Land Resources Map for Mawsynram Block of East Khasi Hills District of Meghalaya, India
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Effectiveness of Fuzzy Overlay Function for Multi-Criteria Spatial Modeling—A Case Study on Preparation of Land Resources Map for Mawsynram Block of East Khasi Hills District of Meghalaya, India

机译:模糊覆盖函数在多标准空间建模中的有效性-以印度梅加拉亚邦东卡西希尔斯山地区Mawsynram区块土地资源图的制作为例

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Multi-criteria spatial modeling is one of the important components of spatial decision support system (SDSS). Multi-criteria spatial modeling often requires a common scale of values for diverse and dissimilar inputs to create an integrated analysis. Weighted overlay function is most commonly used for site suitability analysis which identifies the most preferred locations for a specific phenomenon. However, weighted overlay function gives inconsistent and erroneous results for highly dissimilar inputs as it assumes that most favorable factors result in the higher values of raster, while identifying the best sites. This paper conveys the effectiveness of fuzzy overlay function for multi-criteria spatial modeling. It is based on the principle of fuzzy logic theory which defines membership using Gaussian function on each of the input rasters instead of giving individual rank to them like in weighted overlay function. A case study on preparation of land resources map for Mawsynram block of East Khasi Hills district of Meghalaya, India is presented here. It was observed that fuzzy overlay function has given more satisfactory output in terms of site suitability while comparing with the result of weighted overlay function.
机译:多准则空间建模是空间决策支持系统(SDSS)的重要组成部分之一。多准则空间建模通常需要针对不同和不同输入的通用值尺度来创建集成分析。加权覆盖函数最常用于站点适应性分析,它确定特定现象的最优选位置。但是,加权叠加功能对于高度不同的输入会给出不一致和错误的结果,因为它假定最有利的因素导致较高的栅格值,同时又确定了最佳的位置。本文传达了模糊覆盖函数在多准则空间建模中的有效性。它基于模糊逻辑理论的原理,该原理使用高斯函数在每个输入栅格上定义从属关系,而不是像加权覆盖函数那样给它们单独排序。本文以印度梅加拉亚邦东卡西希尔斯地区的毛维森兰姆区块土地资源图制作为例。据观察,与加权覆盖函数的结果相比,模糊覆盖函数在位置适合性方面给出了更令人满意的输出。

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