DISASTER RISK REDUCTION
; LOGISTIC-REGRESSION
; FREQUENCY RATIO
; URBAN FLOOD
; SPATIAL PREDICTION
; STATISTICAL-MODELS
; CLIMATE-CHANGE
; LAND-COVER
; GIS
; AREAS
WOS学科分类:
Geosciences, Multidisciplinary
; Meteorology & Atmospheric Sciences
; Water Resources
WOS研究方向:
Geology
; Meteorology & Atmospheric Sciences
; Water Resources
英文摘要:
Flooding is a deleterious phenomenon that induces detrimental impacts on humans, properties and environment. As a result, the knowledge of susceptible places and hazard perception is increasingly pertinent. This study mainly aims at identifying areas susceptible to flood through the application of logistic regression model using remote sensing data (RS) and Geographical Information System (GIS). A flood inventory was generated using 153 historical flood locations and a total of 10 predicting factors (elevation, slope, aspect, profile curvature, distance from rivers, distance from roads, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Soil Index (NDSI), the Topographic Wetness Index (TWI) and rainfall) were utilized. Flood points were randomly subdivided into training (75%) for model building and testing (25%) points for validation through the Area Under Curve (AUC) approach. The results indicated that NDVI and rainfall are the most influencing variables for estimating flood risk as they showed a high positive relationship with flood occurrence in the study area. Testing datasets disclosed 79.8% of prediction rate using the AUC. Moreover, the results have been linked with community perception on flood and the outcome revealed that the government is perceived as responsible for all flood mitigation measures instead of being a shared responsibility. This perception may contribute to the increase in susceptibility. The results of this study will be essential for upcoming development projects from different organizations operating in many developing countries and would assist as a baseline for flood risk reduction and management especially for Rwanda.
1.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, 818 South Beijing Rd, Urumqi 830011, Xinjiang, Peoples R China 2.Chinese Acad Sci, Ili Stn Watershed Ecosyst Res, Urumqi 830011, Xinjiang, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100039, Peoples R China 4.Univ Lay Adventists Kigali UNILAK, Fac Environm Sci, POB 6392, Kigali, Rwanda 5.CAS Res Ctr Ecol & Environm Cent Asia, 818 South Beijing Rd, Urumqi 830011, Xinjiang, Peoples R China 6.Chinese Acad Sci, Xinjiang Reg Ctr Resources & Environm Sci Instrum, Urumqi 830011, Peoples R China 7.Univ Rwanda, Coll Agr Anim Sci & Vet Med, POB 117, Huye, Rwanda
Recommended Citation:
Mind',je, Richard,Li, Lanhai,et al. Flood susceptibility modeling and hazard perception in Rwanda[J]. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION,2019-01-01,38