DOI: 10.1007/s11069-019-03816-9
论文题名: Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran
作者: Rezaei M. ; Amiraslani F. ; Samani N.N. ; Alavipanah K.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 100, 期: 1 起始页码: 363
结束页码: 385
语种: 英语
中文关键词: Aggregation methods
; Flooding
; Fuzzy analytic hierarchical processes
; Mamdani fuzzy inference system
; Modeling
英文关键词: accuracy assessment
; data set
; efficiency measurement
; flooding
; fuzzy mathematics
; hierarchical system
; identification method
; numerical model
; pixel
; spatial data
; Iran
; Tehran [Iran]
; Tehran [Tehran (PRV)]
英文摘要: Flooding is one of the most problematic natural events affecting urban areas. In this regard, developing flooding models plays a crucial role in reducing flood-induced losses and assists city managers to determine flooding-prone areas (FPAs). The aim of this study is to investigate on the prediction capability of fuzzy analytical hierarchy process (FAHP) and Mamdani fuzzy inference system (MFIS) methods as two completely and semi-knowledge-based models to identify FPAs in Tehran, Iran. Six flooding conditioning factors including density of channel, distance from channel, land use, elevation, slope, and water discharge were extracted from various geo-spatial datasets. A total of 62 flooding locations were identified in the study area based on the existing reports and field surveys. Of these, 44 (70%) floods were randomly selected as training data and the remaining 18 (30%) cases were used for the validation purposes. After the data preparation step, data were processed by means of two statistical (FAHP) and soft computing (MFIS) methods. Unlike most statistical and soft computing approaches which use flooding inventory data for both training and evaluation of models, only conditioning factor was involved in data processing and inventory data were used in the current study to assess models prediction accuracy. Also, the efficiency of two approaches was evaluated by pixel matching (PM) and area under curve to validate the prediction capability of models. The prediction rate for MFIS and FAHP was 89% and 84%, respectively. Moreover, according to the results obtained from PM, it was found out that about 90% of known flooding locations fell in high-risk areas, whereas it was 83% for FAHP, indicating that flooding susceptibility map of MFIS has higher performance. © 2019, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168698
Appears in Collections: 气候变化与战略
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作者单位: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran; NUIST-Reading Institute, Nanjing University of Information, Science and Technology, Nanjing, Jiangsu, China
Recommended Citation:
Rezaei M.,Amiraslani F.,Samani N.N.,et al. Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran[J]. Natural Hazards,2020-01-01,100(1)