Multi-Index Evaluation for Flood Disaster from Sustainable Perspective
A Case Study of Xinjiang in China
Bibliographic Data
| ID | 15475595 |
|---|---|
| Authors | Yudan Dou (0000-0002-2654-6322, Harbin Institute of Technology), Xiaolong Xue (0000-0002-2689-4298, Harbin Institute of Technology), Zebin Zhao (0000-0001-7983-0610, Harbin Institute of Technology, corresponding author), Xiaowei Luo (0000-0002-6693-3765, City University of Hong Kong), Ankang Ji (0000-0002-8043-804X, Harbin Institute of Technology), Ting Luo (0000-0003-0911-5835, Harbin Institute of Technology) |
| Year | 2018 |
| Volume | 15 |
| Issue | 9 |
| Pages | 1983-1983 |
| Publication date | 2018-09-11 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph15091983 |
| PMID | 30208662 |
| OpenAlex | W2890654020 |
| Language | EN |
| Citations received | 1 |
| References cited | 3 |
The floods have undermined the sustainable construction of cities because of their sudden and destruction. To reduce the losses caused by floods, it is necessary to make a reasonable evaluation for historical floods and provide scientific guidance for future precaution. Previous research mainly used subjective/objective weights or barely made static analysis without considering the uncertainty and ambiguity of floods. Therefore, this study proposed a variable fuzzy recognition model, based on combined weights, to evaluate floods, including the determination of index weights and the choice of evaluation model. To make the index weights reflect both subjective experience and objective data, the combined weights were proposed and calculated based on the principle of minimum identification information. Then, the relative membership degree matrix and evaluation results can be worked out by the variable fuzzy recognition model. Conclusions indicated that the combined weights were more convincing than simply subjective or objective weights. Moreover, the variable fuzzy recognition model, by changing model parameters, got stable evaluation results of the sample data. Therefore, the model can improve the credibility of evaluation and the conclusions can provide reasonable suggestions for management departments
Ambiguity · Credibility · Data mining · Flood myth · Fuzzy logic · Geography · Identification (biology · Index (typography · Operations research · Perspective (graphical · Sustainable development · Variable (mathematics · Computer Science · Flood Risk Assessment and Management · Hydrological Forecasting Using AI · Hydrology and Drought Analysis · Mathematics · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,25 |
| Citation span | 2022 - 2022 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |