Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran

Rahmati, Omid; Pourghasemi, Hamid Reza; Zeinivand, Hossein

doi:10.1080/10106049.2015.1041559

Cited by 463 publications

(200 citation statements)

References 115 publications

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“…In addition, using GIS platform, the support vector machine with different kernel types were applied in Kuala Terengganu basin, Malaysia mainly using topographical input data (Tehrany et al 2015). Frequency ratio, weights-of-evidence models were also applied to Golastan Province, Iran (Rahmati et al 2016a), and in GIS environment, statistical models were integrated for flood-susceptibility mapping (Blanco-Vogt & Schanze 2014; Morelli et al 2014). For flood mapping, many researchers exerted modelling techniques such as hydrologic modelling, hydraulic modelling and global hydrodynamic modelling (Grimaldi et al 2013;Papaioannou et al 2013;Chini et al 2014;Curebal et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Lee

Kim

Jung

et al. 2017

Geomatics, Natural Hazards and Risk

289

128

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Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.

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Section: Introductionmentioning

confidence: 99%

Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Lee

Kim

Jung

et al. 2017

Geomatics, Natural Hazards and Risk

289

128

View full text Add to dashboard Cite

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“…Food hazard maps are useful tools for planning the future direction of city growth, and are usually used to identify flood-susceptible areas (B€ uchele et al 2006;Vahidniaa et al 2008;Tehrany et al 2014a;Rahmati et al 2015). Flood zonation mapping and hazard analysis for different areas often involve multiple criteria/ factors that have to be geographically related to one another (Booij 2005;Minea 2013;Xu et al 2013;Poussin et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Rahmati

Zeinivand

Besharat

2015

Geomatics, Natural Hazards and Risk

Self Cite

352

136

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Flood is considered to be the most common natural disaster worldwide during the last decades. Flood hazard potential mapping is required for management and mitigation of flood. The present research was aimed to assess the efficiency of analytical hierarchical process (AHP) to identify potential flood hazard zones by comparing with the results of a hydraulic model. Initially, four parameters via distance to river, land use, elevation and land slope were used in some part of the Yasooj River, Iran. In order to determine the weight of each effective factor, questionnaires of comparison ratings on the Saaty's scale were prepared and distributed to eight experts. The normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods. The set of criteria were integrated by weighted linear combination method using ArcGIS 10.2 software to generate flood hazard prediction map. The inundation simulation (extent and depth of flood) was conducted using hydrodynamic program HEC-RAS for 50-and 100-year interval floods. The validation of the flood hazard prediction map was conducted based on flood extent and depth maps. The results showed that the AHP technique is promising of making accurate and reliable prediction for flood extent. Therefore, the AHP and geographic information system (GIS) techniques are suggested for assessment of the flood hazard potential, specifically in no-data regions.

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“…Various methods have been used for flood susceptibility mapping. Recently, multi-criteria evaluation [26], decision tree (DT) analysis [27], fuzzy theory [28,29], weights-of-evidence (WoE) [30], artificial neural network (ANN) [31][32][33], frequency ratio (FR) [34], and logistic regression (LR) [35] approaches have been widely used by many researchers. Map products represent the regions of study areas that are susceptible to flooding using GIS software.…”

Section: Introductionmentioning

confidence: 99%

Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas

et al. 2016

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This study focused on producing flash flood hazard susceptibility maps (FFHSM) using frequency ratio (FR) and statistical index (SI) models in the Xiqu Gully (XQG) of Beijing, China. First, a total of 85 flash flood hazard locations (n = 85) were surveyed in the field and plotted using geographic information system (GIS) software. Based on the flash flood hazard locations, a flood hazard inventory map was built. Seventy percent (n = 60) of the flooding hazard locations were randomly selected for building the models. The remaining 30% (n = 25) of the flooded hazard locations were used for validation. Considering that the XQG used to be a coal mining area, coalmine caves and subsidence caused by coal mining exist in this catchment, as well as many ground fissures. Thus, this study took the subsidence risk level into consideration for FFHSM. The ten conditioning parameters were elevation, slope, curvature, land use, geology, soil texture, subsidence risk area, stream power index (SPI), topographic wetness index (TWI), and short-term heavy rain. This study also tested different classification schemes for the values for each conditional parameter and checked their impacts on the results. The accuracy of the FFHSM was validated using area under the curve (AUC) analysis. Classification accuracies were 86.61%, 83.35%, and 78.52% using frequency ratio (FR)-natural breaks, statistical index (SI)-natural breaks and FR-manual classification schemes, respectively. Associated prediction accuracies were 83.69%, 81.22%, and 74.23%, respectively. It was found that FR modeling using a natural breaks classification method was more appropriate for generating FFHSM for the Xiqu Gully.

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Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran

Cited by 463 publications

References 115 publications

Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas

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