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Assessment of food hazard mapping in urban areas using entropy weighting method: a case study in Hamadan city, Iran

Sepehri Mehdi, Malekinezhad Hossein, Hossein Seyed Zeynalabedin, Reza Ildoromi Ali

Acta Geophysica

2019

Vol. 67, no. 5

1435--1449

Flood is one of the major natural disasters which cause enormous casualties and damages particularly in urban areas. In urban areas, studies on food hazards have been accompanied by tensions for various reasons, including complexity of urban levels, diferent spatial modeling indices, lack of accurate hydrological data, and precise modeling of land surface simulations. This paper used a Constrained Delaunay Triangular Irregular Network to model fne urban surfaces (based on the detailed ground sampling data), and subsequently discusses fve indicators regarding the dangers of food, namely (1) elevation, (2) slope, (3) distance to discharge channels, (4) index of development and persistence of the drainage network (IDPR), and (5) infiltration rate. In the next step for food hazard mapping, the combination of geographical information systems and the entropy weight method as the multi-criteria decision analysis was used to combine the indicators. The proposed methodology was used for Hamadan city that is located in the central part of Hamadan Province in Iran where several foods occur annually. The food hazard mapping indicates that approximately 15.83% of the total study area is classifed as very highly hazardous, 31.72% as hazardous, 20.11% as moderate, 16.02% as minor, and 16.32% as the least hazardous. Finally, superimposition and receiver operating characteristic (ROC) curve methods were used to verify the accuracy of the obtained food hazard map. In terms of superimposition and ROC curve, the accuracy of the model was approximately 70% and 73%, respectively.

Determination of pumped storage capacity combining the entropy weighting method and principal component analysis

Zhang Z., Cai X., Parkun D.

Journal of Power Technologies

2014

Vol. 94, nr 3

165--175

The aim of the study is to evaluate methods for determining appropriate pumped storage capacities. This is especially relevant, since pumped storage units are, today, viewed as some of the best means of storing large amounts intermittently-produced power in order to meet peak demands on power supply grids. The determination of appropriate pumped storage capacity is a problem of integrated decision-making. The entropy weighting method and principal component analysis are combined to determine the optimum pumped storage capacity, taking into account several representative indices, whilst using the syntropy method to standardize the data indicators. The entropy weighting method is used to determine the weighting of the indicators, while principal component analysis offers reduction of the dimensions. The optimal solution is then determined by ranking the evaluation values for each design. This method can avoid subjectivity in the weighting assignment and simplifies the calculations to an evaluation problem which contains multiple evaluation indices. Using the principle of energy-saving scheduling, the peak-shaving method is applied to the dispatching over a typical daily load in order to verify the rationality of the calculated pumped storage capacity. The example analysis, here, shows that it is reasonable to determine the optimum pumped storage capacity by using this combination of the entropy weighting method and principal component analysis.