Runoff estimation using digital image processing for residential areas

Pramod Soni, Hemanta Medhi, Anitya Sagar, Pulkit Garg, Abhay Singh, Umesh Karna
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Abstract

With the burgeoning population worldwide, the demand for freshwater supply is increasing, mostly in urban areas, due to the influx of people for better livelihood. To mitigate this burden of freshwater demand and build a sustainable water management system, harvesting rainwater during the rainfall season is a viable option. Runoff estimation studies in the past are time-intensive as parameter estimation for an area is complex by the conventional method. In this study, the Motilal Nehru National Institute of Technology (MNNIT), Allahabad campus was selected as a pilot project to assess a methodology that uses Google Earth images for obtaining the runoff coefficients. This method is easy and consumes less time in runoff estimation. This was compared with the conventional method. Using the conventional method (Arc-GIS), the equivalent runoff coefficients for these catchments were found to be 0.2780, 0.3553, and 0.4111, respectively. The range of error (compared to the traditional method) in runoff obtained from the proposed method with a default k value (0.8) was found to be 8.16–13.55%, with an average value of 9.91%. However, with a slightly modified value of k (0.9), the errors were significantly reduced to 1.94–3.32%, with an average of 2.15%.
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基于数字图像处理的居住区径流估算
随着世界人口的迅速增长,对淡水供应的需求正在增加,主要是在城市地区,这是由于人们为了更好的生活而涌入。为了减轻这种淡水需求负担并建立可持续的水管理系统,在降雨季节收集雨水是一个可行的选择。以往的径流估算研究由于传统方法对一个区域的参数估算比较复杂,需要耗费大量的时间。在这项研究中,阿拉哈巴德的Motilal Nehru国立理工学院(MNNIT)被选为试点项目,以评估使用谷歌地球图像获取径流系数的方法。该方法简便、耗时短。并与常规方法进行了比较。利用传统方法(Arc-GIS),这些流域的等效径流系数分别为0.2780、0.3553和0.4111。在缺省k值为0.8的情况下,该方法得到的径流误差范围(与传统方法相比)为8.16 ~ 13.55%,平均值为9.91%。然而,稍微修改k值(0.9),误差显著降低到1.94-3.32%,平均为2.15%。
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