Ranking of Storm Water Harvesting Sites Using Heuristic and Non-Heuristic Weighing Approaches

Abstract

Conservation of water is essential as climate change coupled with land use changes influence the distribution of water availability. Stormwater harvesting (SWH) is a widely used conservation measure, which reduces pressure on fresh water resources. However, determining the availability of stormwater and identifying the suitable sites for SWH require consideration of various socio-economic and technical factors. Earlier studies use demand, ratio of runoff to demand and weighted demand distance, as the screening criteria. In this study, a Geographic Information System (GIS) based screening methodology is adopted for identifying potential suitable SWH sites in urban areas as a first pass, and then a detailed study is done by applying suitability criteria. Initially, potential hotspots are identified by a concept of accumulated catchments and later the sites are screened and ranked using various screening parameters namely demand, ratio of runoff to demand and weighted demand distance. During this process, the opinion of experts for finalizing the suitable SWH sites brings subjectivity in the methodology. To obviate this, heuristic (Saaty Analytic hierarchy process (AHP)) and non-heuristic approaches (Entropy weight, and Principal Component Analysis (PCA) weighing techniques) are adapted for allotting weights to the parameters and applied in the ranking of SWH sites in Melbourne, Australia and Dehradun, India. It is observed that heuristic approach is not effective for the study area as it was affected by the subjectivity in the expert opinion. Results obtained by non-heuristic approach come out to be in a good agreement with the sites finalized for SWH by the water planners of the study area. Hence, the proposed ranking methodology has the potential for application in decision making of suitable storm water harvesting sites.

Spatiotemporal Variations of Land Use and Landscape Ecological Risk in a Resource-Based City, from Rapid Development to Recession

“…In this study, the entropy weight method is employed for allotting weights to the landscape metrics of different periods. This method is regarded as a relatively objective and effective method for obtaining the contribution of each index to the landscape disturbance indices [34][35]…”

Intelligent Evaluation System of Water Inrush in Roadway (Tunnel) and Its Application

“…The entropy-weight of each assessment index can be calculated by using information entropy based on the degree of variation of each indicator [35,36]. Then, the weight of each index is modified by the method of entropy to obtain a more objective index weight.…”

Comparison and Analysis of Different Discrete Methods and Entropy-Based Methods in Rain Gauge Network Design

“…With the development of the entropy theory in hydrological analysis [7][8][9], many types of information entropy have been developed that can reflect the degree of information correlation between stations. This information entropy has been gradually applied to the design of rain gauge networks with the advantages of efficient calculation, clear theory and high practicability [10][11][12][13]. However, the process of calculating probability density from rainfall data as continuous data is complex when calculating information entropy.…”