Open AccessFlood risk analysis using fuzzy models
Author(s) -
Nandalal H.K.,
Ratnayake U.R.
Publication year - 2011
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/j.1753-318x.2011.01097.x
Subject(s) - flood myth , vulnerability (computing) , flooding (psychology) , 100 year flood , hazard , flood risk assessment , environmental science , return period , hydrology (agriculture) , fuzzy logic , water resource management , risk assessment , population , drainage basin , risk analysis (engineering) , computer science , geography , cartography , geology , business , geotechnical engineering , environmental health , archaeology , artificial intelligence , medicine , psychology , chemistry , computer security , organic chemistry , psychotherapist
Abstract Risk is a combination of the factors that determine vulnerability and exposure potential for people to a hazard. The computation of flood extents and the identification of vulnerable elements help to determine high‐risk zones due to floods in advance, which helps to take mitigatory measures effectively and efficiently. This study examines how effectively the risk with respect to floods can be assessed using a fuzzy approach taking the frequently flooding Kalu‐Ganga River basin in Sri Lanka as the study area. Flood extent for a 100‐year return period rainfall was determined using Hydrologic Engineering Center's Hydrologic Modelling System (HEC‐HMS)‐ and HEC‐River Analysis System‐based models. Flood extent and mean flood depth were taken as hazard indicators while population density and dependency ratio were used as vulnerability indicators. Based on these indicators, flood risk was determined for the lowest administrative divisions within the inundated area using conventional risk assessment approaches. A methodology was proposed and applied to assess risk assuming the above indicators as fuzzy variables. Comparison of the results obtained from the two approaches indicates the proposed fuzzy‐based method, which takes uncertainty in the determination of hazard, vulnerability and risk levels into account, as providing more accurate results.