Prediction of Flash Flood Susceptibility Using Fuzzy Analytical Hierarchy Process (Fahp) Algorithms and Gis: A Study Case of Guelmim Region in Southwestern of Morocco

Talha, Syed; Maanan, Mehdi; Atika, H.; Rhinane, Hassan

doi:10.5194/isprs-archives-xlii-4-w19-407-2019

Cited by 27 publications

(18 citation statements)

References 35 publications

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“…The results of the discriminant analysis (F1, Table 8) suggest that the time of concentration "Tc" contributes to the discrimination of the drainage network patterns only in the central Anti-Atlas sector. This result is in agreement with several studies that mention the influence of the drainage network patterns on the flooding risks sensitivity of arid zones in Morocco or in other regions of the world [35,49,50].…”

Section: Drainage Network and Hydrological Responsesupporting

confidence: 93%

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

et al. 2020

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Drainage network patterns influence the hydrological response of the watersheds and must be taken into account in the management of the water resource. In this context, it is important to identify the factors that control the configuration of drainage networks in and beyond specific climatic conditions. Here, we study 318 basins spread over three sectors (arid, semi-arid, and semi-humid) of Morocco where seven drainage network patterns have been identified. From each basin, 14 parameters were extracted, describing the relief, geology, morphometry, drainage network, land cover, precipitation, and time of concentration (Tc). Principal component analysis (PCA) and discriminant analysis (DA) processing were performed on the entire database and on each sector separately. The results show that the drainage network pattern is a feature of the landscape that contributes significantly to the variance of the basins. They suggest that the distribution of network patterns is controlled by the relationship between the different parameters, mainly those related to the relief, more than by the variations of each parameter taken individually. The network discrimination rate is 63.8%, which improves when each sector is treated separately. Confusion in discrimination are similar across all sectors and can be explained by similar conditions (active tectonic, deformation, and uplift) or transitions from one network pattern to another, due to the landscape evolution of certain sectors. A contribution of climatic variables appears locally but was attributed to a statistical coincidence, these parameters presenting a distribution close to that of the relief and geology variables.

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Section: Drainage Network and Hydrological Responsesupporting

confidence: 93%

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

et al. 2020

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“…This factor computed based on DEM .According to the literature, slope is regarded as one of the factors that has the biggest effect on flooding ( (Rahmati et al, 2015), (Vahid et al, 2018) (Talha et al, 2019) ) . In addition slope controls water flow velocity (Costache et al, 2020b) and has been classified in this study into five classes with resolution of 10 m (Figure 3b).…”

Section: Slopementioning

confidence: 99%

“…(Organization, 2014). Morocco is not an exception, it has faces also innumerable flash flood events, the most important are Oued Ourika floods on 1987,1989and 1995(Atlas et al, 2014 , those of Mohammedia on 1996, 2002(Chaabane et al, 2017 , Guelmim region floods on 2002,2010 and 2014 (Talha et al, 2019) and the last event was in Tetouan on March 1st, 2021 . Nowadays, the vulnerability of flash floods is very high, it is necessary to think systematically about risk management (Panahi et al, 2021), starting with understanding the elements that influence the increase of this disaster, and then identifying flood prone areas.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, the vulnerability of flash floods is very high, it is necessary to think systematically about risk management (Panahi et al, 2021), starting with understanding the elements that influence the increase of this disaster, and then identifying flood prone areas. Many researchers strive to achieve these goals using several methods, starting with older method based on simple statistics such as frequency ratio, weight of evidence (Shafapour Tehrany et al, 2017 ;Talha et al, 2019 ;Radwan et al, 2019 ;Swain et al, 2020), and ending with artificial intelligence , machine learning approach (Ma et al, 2019 ;Arabameri et al, 2020Costache et al, 2020Elmahdy et al, 2020 ;Costache et al, 2021 ). The machine learning approach is a very effective method in terms of accurate identification and modeling , it attempts to solve flash flood problems by finding the relationship between the flood risk and its factors rather than direct determination of weights (Ma et al, 2019) and that, by using several algorithms such as Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB) and Artificial Neural Network (ANN).…”

Section: Introductionmentioning

confidence: 99%

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Performance of Machine Learning Algorithms for Mapping and Forecasting of Flash Flood Susceptibility in Tetouan, Morocco

Sellami

Maanan

Rhinane

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Since the industrial revolution, the world is experiencing a huge change in its climate, which causes many imbalances such as flash floods (FF). The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco. For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421 non-flood points) and 9 flash-flood predictors (Elevation , Slope , Aspect , LU/LC , Stream Power Index , Plan curvature , Profile Curvature , Topographic Position Index and Topographic Wetness Index ) that were extracted from the DEM (10m resolution) and satellite imagery (Sentinel 2B) of the study area . Models were trained on 70% and tested on 30% of this dataset also they were evaluated using several metrics such as the Receiver Operating Characteristic (ROC) Curve, precision, recall, score and kappa index. The result demonstrated that RF (AUC = 0.99, Accuracy = 96%, Kappa statistics = 0.92) has the highest performance, followed by ANN (AUC = 0.98, Accuracy = 95%, Kappa statistics = 0.89) and SVM (AUC = 0.96, Accuracy = 92%, Kappa statistics = 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster.

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“…The distance of the road from the river is a consideration in choosing an evacuation route. Drainage [9] and surface roughness [10] are also considered to determine the level of vulnerability of an area due to flooding. Floods that occur in developing countries are a result of human activity.…”

Section: Introductionmentioning

confidence: 99%

Priority search simulation for flood evacuation routes using fuzzy AHP approach

Chandrawati¹,

Ratna²,

Sari³

2022

J Appl Eng Science

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A flood is an event of an increase in water volume above the standard limit due to increased rainfall, rising sea levels, storms, and others that result in submerging an area. Floods are disasters that can cause damage and loss of property, disrupt community activities and even cause loss of life. The central defiance to rescue flood victims is choosing a safe route for flood victims to reach the evacuation site. To be able to choose a safe route for flood victims, a flood evacuation simulation is made. Flood evacuation simulation is part of the game that has been created and aims to provide education about the weight of the obstacle that needs to be considered in selecting routes for flood victims. In this flood evacuation simulation, each road has obstacles. The method proposed for choosing safe routes for flood victims is the Fuzzy-based Analytical Hierarchy Process (Fuzzy AHP). The calculation of road route weight using the Fuzzy AHP method will produce the weight for each route. The smallest weight route shows the priority route and the safe route for flood victims to pass. In this case, the Fuzzy AHP method's calculation produces the lowest weight of 0.02347, which is achieved by route 5, the route passing through S-a-b-d-D. This route is a priority route that is safe for flood victims to pass through.

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Prediction of Flash Flood Susceptibility Using Fuzzy Analytical Hierarchy Process (Fahp) Algorithms and Gis: A Study Case of Guelmim Region in Southwestern of Morocco

Cited by 27 publications

References 35 publications

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

Drainage Network Patterns Determinism: A Comparison in Arid, Semi-Arid and Semi-Humid Area of Morocco Using Multifactorial Approach

Performance of Machine Learning Algorithms for Mapping and Forecasting of Flash Flood Susceptibility in Tetouan, Morocco

Priority search simulation for flood evacuation routes using fuzzy AHP approach

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