scholarly journals PRELIMINARY INVESTIGATIONS ON FLOOD SUSCEPTIBILITY MAPPING IN ANKARA (TURKEY) USING MODIFIED ANALYTICAL HIERARCHY PROCESS (M-AHP)

2018 ◽  
Vol XLII-5 ◽  
pp. 361-365 ◽  
Author(s):  
B. Sozer ◽  
S. Kocaman ◽  
H. A. Nefeslioglu ◽  
O. Firat ◽  
C. Gokceoglu
Keyword(s):  
Urban Areas ◽  
Flood Hazard ◽  
Analytic Solutions ◽  
Drainage Density ◽  
Susceptibility Mapping ◽  
Topographic Wetness Index ◽  
Flood Susceptibility ◽  
Spatial Datasets ◽  
Flood Susceptibility Mapping ◽  
Hierarchy Process

<p><strong>Abstract.</strong> Susceptibility mapping for disasters is very important and provides the necessary means for efficient urban planning, such as site selection and the determination of the regulations, risk assessment and the planning of the post-disaster stage, such as emergency plans and activities. The main purpose of the present study is to introduce the preliminary results of an expert based flood susceptibility mapping approach applied in urban areas in case of Ankara, Turkey. The proposed approach is based on Modified Analytic Hierarchy Process (M-AHP), which is an expert-based algorithm and provides data based modeling. The existing spatial datasets are evaluated in the decision process and the specified number of decision points according to the degree desired can be formed. The parameter priorities can be identified at the beginning of the modeling with this approach by the responsible expert. The spatial datasets used in the modeling and mapping process have been provided by the General Directorate of Mapping (HGM). Additionally, the slope gradient of topography, drainage density, and topographic wetness index of the site being one of the second derivatives of topography have been evaluated to identify the main conditioning factors controlling water accumulation on ground. Considering the uncertainties in flood hazard assessment and limitations in sophisticated analytic solutions, the proposed methodology could be evaluated to be an efficient tool to detect the most influential parameters representing the flood vulnerability and assessing the mitigation applications in urban environment.</p>

scholarly journals Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 364 ◽  
Author(s):  
Matej Vojtek ◽  
Jana Vojteková
Keyword(s):  
Analytical Hierarchy Process ◽  
River Network ◽  
Susceptibility Mapping ◽  
Mitigation Strategies ◽  
Network Density ◽  
Flow Accumulation ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
Hierarchy Process ◽  
Very High

Flood susceptibility mapping and assessment is an important element of flood prevention and mitigation strategies because it identifies the most vulnerable areas based on physical characteristics that determine the propensity for flooding. This study aims to define the flood susceptibility zones for the territory of Slovakia using a multi-criteria approach, particularly the analytical hierarchy process (AHP) technique, and geographic information systems (GIS). Seven flood conditioning factors were chosen: hydrography—distance from rivers, river network density; hydrology—flow accumulation; morphometry—elevation, slope; and permeability—curve numbers, lithology. All factors were defined as raster datasets with the resolution of 50 x 50 m. The AHP technique was used to calculate the factor weights. The relative importance of the selected factors prioritized slope degree as the most important factor followed by river network density, distance from rivers, flow accumulation, elevation, curve number, and lithology. It was found that 33.1% of the territory of Slovakia is characterized by very high to high flood susceptibility. The flood susceptibility map was validated against 1513 flood historical points showing very good agreement between the computed susceptibility zones and historical flood events of which 70.9% were coincident with high and very high susceptibility levels, thus confirming the effectiveness of the methodology adopted.

scholarly journals Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud

2020 ◽  
Vol 9 (12) ◽  
pp. 720 ◽  
Author(s):  
Kishore Chandra Swain ◽  
Chiranjit Singha ◽  
Laxmikanta Nayak
Keyword(s):  
Google Earth ◽  
Susceptibility Mapping ◽  
Flood Susceptibility ◽  
Land Cover Dynamics ◽  
Risk Zones ◽  
Flood Susceptibility Mapping ◽  
High Flood ◽  
Hierarchy Process ◽  
Pair Wise Comparison ◽  
Very High

Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic information system (GIS)/remote sensing (RS) with a cloud computing API on the Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric, permeability, land cover dynamics, and anthropogenic interference, which further had 21 sub-criteria. The relative importance of each criterion prioritized as per their contribution toward flood susceptibility and weightage was given by an AHP pair-wise comparison matrix (PCM). The most and least prominent flood-causing criteria were hydrologic (0.497) and anthropogenic interference (0.037), respectively. An area of ~3000 sq km (40.36%) was concentrated in high to very high flood susceptibility zones that were in the vicinity of rivers, whereas an area of ~1000 sq km (12%) had very low flood susceptibility. The GIS-AHP technique provided useful insights for flood zone mapping when a higher number of parameters were used in GEE. The majorities of detected flood susceptible areas were flooded during the 2019 floods and were mostly located within 500 m of the rivers’ paths.

Novel Bayesian Additive Regression Tree methodology for flood susceptibility modeling

2021 ◽  
Author(s):  
Saeid Janizadeh ◽  
Mehdi Vafakhah ◽  
Zoran Kapelan ◽  
Naghmeh Mobarghaee Dinan
Keyword(s):  
Predictive Accuracy ◽  
Flood Hazard ◽  
Regression Tree ◽  
Predictive Performance ◽  
Curve Number ◽  
Drainage Density ◽  
Topographic Wetness Index ◽  
Hazard Management ◽  
Flood Susceptibility ◽  
Additive Regression

Abstract Identifying areas prone to flooding is a key step in flood hazard management. The purpose of this study is to develop and present a novel flood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fifteen climatic and geo-environmental variables were used as inputs into all flood susceptibility models: altitude, aspect, slope, plan curvature, profile curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing flood field survey and other information available for the analyzed area, a total of 118 flood locations were identified as potentially prone to flooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the altitude and distance from the river are the most important variables for assessing flooding susceptibility.

scholarly journals Modelling Of Food Hazard Susceptibility in Komatipoort, South Africa

2018 ◽  
Vol 37 (1) ◽  
pp. 47-71
Author(s):  
Nomcebo Khumalo ◽  
Aloyce W. Mayo ◽  
Subira Munishi
Keyword(s):  
Hydrological Modeling ◽  
Small Town ◽  
Flood Hazard ◽  
Central Business District ◽  
Hydraulic Modeling ◽  
Susceptibility Mapping ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
Using Data ◽  
Business District

Komatipoort, a small town located at the confluence of Komati and Krokodil rivers, is constantly being hit by floods which affect the residents of this small town as well as the farmers settling along the rivers. This study aimed at mapping the flood hazard and susceptibility through the integration of GIS techniques and hydraulic modeling. Due to inconsistency in the length of streamflow data in the different gauging stations, a Hydrological modeling HBV model, was utilized for modelling runoff in order to extend flow records at station X2HO32 for Komati River. Calibration was conducted using observed data from 1982 to 1993, giving an efficiency value of 65% and validation was done using data from 1993 to 1999, giving an efficiency value of 53%. Flood frequencies were analyzed and flood quantiles were determined at different return periods. HEC-RAS was utilized to simulate the hydraulic parameters of Komati and Krokodil rivers to obtain flood hazard maps. GIS-based multi-criteria analysis techniques were incorporated for flood susceptibility mapping. Hydraulic analysis showed that the floods mostly affect the farms and settlements along the rivers and a small part of the central business district is affected. Flood susceptibility mapping showed that the area is generally highly susceptible to flooding because of a combination of anthropogenic and natural factors.

scholarly journals GIS-Based Flood Susceptibility Mapping Using Statistical Index and Weighting Factor Models

2021 ◽  
Vol 19 (6) ◽  
pp. 1-13
Author(s):  
Worawit Suppawimut ◽  
Keyword(s):  
Land Use ◽  
Weighting Factor ◽  
Susceptibility Mapping ◽  
Slope Aspect ◽  
Economic Losses ◽  
Soil Drainage ◽  
Topographic Wetness Index ◽  
Statistical Index ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

Floods are one of the most devastating natural hazards, causing deaths, economic losses, and destruction of property. Flood susceptibility maps are an essential tool for flood mitigation and preparedness planning. This study mapped flood susceptibility using statistical index (SI) and weighting factor (WF) models in San Pa Tong District, Chiang Mai Province, Thailand. The conditioning factors used to perform flood susceptibility mapping were elevation, slope, aspect, curvature, topographic wetness index, stream power index, rainfall, distance from rivers, stream density, soil drainage, land use, and road density. The flood data were randomly classified as training data for mapping (70% of data) and testing data for model validation (30% of data). The results revealed that the SI and WF models classified 49.49% and 51.74% of the study area, respectively, as very highly susceptible to flooding. In the WF model, the factors with the greatest influence were land use, soil drainage, and elevation. The validation of the models using the area under the curve revealed that the success rates of the SI and WF models were 91.80% and 93.06%, while the prediction rates were 92.05% and 93.52%, respectively. The results from this study can be useful for local authorities in San Pa Tong District for flood preparedness and mitigation.

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