GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia

Souissi, Dhekra; Zouhri, Lahcen; Hammami, Salma; Msaddek, Mohamed Haythem; Zghibi, Adel; Dlala, Mahmoud

doi:10.1080/10106049.2019.1566405

Cited by 217 publications

(84 citation statements)

References 75 publications

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“…One of the methods used for assessing flood susceptibility is multi-criteria decision analysis (MCDA) [15,16]. Different approaches for assigning weights (e.g., expert judgement) or weighting methods (e.g., pairwise) can be used in MCDA to prioritize the relative importance of the selected conditioning factors [10,17].…”

Section: Introductionmentioning

confidence: 99%

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

Vojtek

Vojteková

2019

Water

198

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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.

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Section: Introductionmentioning

confidence: 99%

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

Vojtek

Vojteková

2019

Water

198

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“…The AHP model is based on the active participation of decision-makers within the entire methodological workflow [60]. Many studies applied this method for the evaluation of susceptibility to other natural hazards [26,36,52,61,62]. By using this method, an unstructured problem is broken into many components.…”

Section: Analytical Hierarchy Process (Ahp)mentioning

confidence: 99%

“…Considering terms of bivariate statistics, the following models can be remarked: frequency ratio (FR) [22], weights of evidence (WoE) [23], statistical index (SI) [24], certainty factor (CF) [1] and index of entropy (IE) [25]. Regarding the multi-criteria decision making (MCDM), the following techniques are frequently used: Analytical Hierarchy Process (AHP) [26], VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [27] and Technique for Order of Prioritization by Similarity to Ideal Solution (TOPSIS) [28].…”

Section: Introductionmentioning

confidence: 99%

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

et al. 2019

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Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.

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“…Thus, several studies have utilized the MCDM technique in assessing and mapping flood susceptible zones [17][18][19][20][21]. Among the MCDM techniques, AHP is the most widely used technique in addressing flood disaster challenges because of its ability to solve a wide range of multiple-criteria decision-making problems using a pairwise comparison matrix to generate priority weights for each decision element [22][23][24][25][26][27].…”

Section: Previous Studiesmentioning

confidence: 99%

“…In Rhodope-Evros region of Greece, Kazakis et al [30] employed AHP to assess flood hazard areas by estimating the weights of seven parameters with flow accumulation having the highest weight (3.0), followed in equal weight (2.1) by drainage distance and elevation. Additionally, in southeastern Tunisia, Souissi et al [27] developed a flood susceptibility map using eight factors with elevation being the most prominent flood occurrence factor (0.23), followed by land use/land cover (0.18). In Abidjan, Cote D'Ivoire, Danumah et al [25] used AHP to assess the risk of flooding and found that the highest weighted parameter was slope (4.31) and then rainfall (4.20).…”

Section: Previous Studiesmentioning

confidence: 99%

Flash Flood Impact Assessment in Jeddah City: An Analytic Hierarchy Process Approach

Dano

2020

Hydrology

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Floods are among the most destructive natural hazards that cost lives and disrupt the socioeconomic activities of residents, especially in the rapidly growing cities of developing countries. Jeddah, a coastal city situated in Saudi Arabia, has experienced severe flash flood events in recent years. With intense rainfall, extensive coastal developments, and sensitive ecosystems, the city is susceptible to severe flash flood risks. The objective of this article is to apply an Analytic Hierarchy Process (AHP) model to explore the impacts of flash flood hazards and identify the most effective approaches to reducing the flash flood impacts in Jeddah using expert’s opinions. The study utilizes experts’ judgments and employs the AHP for data analyses and modeling. The results indicated that property loss has the highest probability of occurrence in the events of a flash flood with a priority level of 42%, followed by productivity loss (28%). Injuries and death were rated the least priority of 18% and 12%, respectively. Concerning flood impact reduction alternatives, river management (41%) and early warning system (38%) are the most favorable options. The findings could assist the government to design appropriate measures to safeguard the lives and properties of the residents. The study concludes by underscoring the significance of incorporating experts’ judgments in assessing flash flood impacts.

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GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia

Cited by 217 publications

References 75 publications

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

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

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

Flash Flood Impact Assessment in Jeddah City: An Analytic Hierarchy Process Approach

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