scholarly journals Application of Remote Sensing Technique and Geographic Information Science for Flood Risk Mapping-A Case Study of the Offinso District, Kumasi-Ghana

2018 ◽  
Vol 07 (01) ◽  
Author(s):  
Emmanuel Oduro Amoako ◽  
Lin Sun
Keyword(s):  
Remote Sensing ◽  
Flood Risk ◽  
Information Science ◽  
Geographic Information Science ◽  
Geographic Information ◽  
Risk Mapping ◽  
Remote Sensing Technique ◽  
Flood Risk Mapping

Flood Vulnerability, Risk, and Susceptibility Assessment

2020 ◽  
pp. 1-27
Author(s):  
Mohd Talha Anees ◽  
Ahmad Farid Bin Abu Bakar ◽  
Lim Hwee San ◽  
Khiruddin Abdullah ◽  
Mohd Nawawi Mohd Nordin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Flood Risk ◽  
Geographic Information ◽  
Susceptibility Assessment ◽  
Flood Vulnerability ◽  
Local Conditions ◽  
Flood Susceptibility ◽  
Flood Risk Mapping

Flood can be assessed through flood vulnerability, risk, and susceptibility analysis using remote sensing, geographic information system, and hydrological modelling. In this chapter, different stages, complexities, and processes of flood vulnerability, risk, and susceptibility assessment were discussed. The study reveals that flood vulnerability should be assessed based on four aspects: physical, social, economic, and environmental. Flood risk should be assessed by three stages: risk analysis, disaster relief, and preparedness, whereas flood susceptibility assessment involves three processes. Overall, it was found that the responsible factors vary as per the local conditions, which need to be carefully analyzed and selected. Furthermore, the role of remote sensing and geographic information system in flood risk management were found important especially in flood risk mapping and in the selection of responsible flooding factors.

Uncertainties in flood risk mapping: a case study on estimating building damages for a river flood in Finland

2010 ◽  
Vol 3 (2) ◽  
pp. 166-183 ◽  
Author(s):  
L. Koivumäki ◽  
P. Alho ◽  
E. Lotsari ◽  
J. Käyhkö ◽  
A. Saari ◽  
...  
Keyword(s):  
Flood Risk ◽  
Risk Mapping ◽  
River Flood ◽  
Flood Risk Mapping

scholarly journals GIS and local knowledge in disaster management: a case study of flood risk mapping in Viet Nam

Disasters ◽  
2008 ◽  
Vol 33 (1) ◽  
pp. 152-169 ◽  
Author(s):  
Phong Tran ◽  
Rajib Shaw ◽  
Guillaume Chantry ◽  
John Norton
Keyword(s):  
Disaster Management ◽  
Local Knowledge ◽  
Flood Risk ◽  
Risk Mapping ◽  
Viet Nam ◽  
Flood Risk Mapping

Flood risk mapping based on hydraulic model 2D and GIS , Case study in Draa Ben Khada, Tizi ouzou, Algeria

2020 ◽  
Vol 11 (2) ◽  
pp. 265-277
Author(s):  
Abdelghani Leghouchi ◽  
◽  
Mohammad Djemai ◽  
Oussama Derdous ◽  
Jamila Tarhouni ◽  
...  
Keyword(s):  
Flood Risk ◽  
Hydraulic Model ◽  
Risk Mapping ◽  
Flood Risk Mapping

scholarly journals Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3115
Author(s):  
Hadi Farhadi ◽  
Mohammad Najafzadeh
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Flood Risk ◽  
Google Earth ◽  
Risk Mapping ◽  
Geographical Information ◽  
Slope Aspect ◽  
Landsat 8 ◽  
Risk Indices ◽  
Flood Risk Mapping

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

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