4.7 Article

Spatial Optimisation Technique for Planning Groundwater Supply Schemes in a Rapid Growing Urban Environment

期刊

WATER RESOURCES MANAGEMENT
卷 28, 期 3, 页码 731-747

出版社

SPRINGER
DOI: 10.1007/s11269-013-0511-0

关键词

Spatial decision support system; Spatial optimisation; Groundwater modelling; Urban water supply; Multi-criteria evaluation

向作者/读者索取更多资源

Uneven distribution of domestic water in space and time is a major concern in many fast growing cities due to improper planning and lack of scientific approach. This problem is much severe where the maximum domestic water requirements are met from the groundwater resources. Optimising a single groundwater pumping scheme may be an easy task using simple linear programming technique but, if the number of pumping schemes and constraints are more, solutions for identifying such groundwater schemes are more difficult and laborious using conventional methods as the constraints varies in space and time. In this paper, a new technique was developed to identify new groundwater pumping schemes to meet the present and future domestic water requirements in space and time by integrating spatial optimisation technique with the groundwater model. The approach considers the possible optimum rate of groundwater pumping, minimising the cost of water supply scheme and having minimum impact on the downstream side groundwater table using high resolution satellite data (IKONOS), Geographical Information System (GIS) tools and optimisation techniques. Dehradun, which is one of the fast growing cities in India, was considered as a study area to demonstrate the proposed new technique. Domestic water demand for next two decades (up to 2,031) was forecasted and compared with the existing supplies. Nearly 48 additional groundwater pumping schemes were identified to cater the present and future demands. Its impact on the groundwater table was also studied using groundwater modelling technique.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据