Artificial Neural Networks for Flood Susceptibility Mapping in Data-Scarce Urban Areas

Falah, Fatemeh; Rahmati, Omid; Rostami, Mohammad; Ahmadisharaf, Ebrahim; Daliakopoulos, Ioannis N.; Pourghasemi, Hamid Reza

doi:10.1016/b978-0-12-815226-3.00014-4

Cited by 114 publications

(61 citation statements)

References 48 publications

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“…Flash Flood is directly related to slope degree and is an essential morphology characteristic (Bannari et al 2016;Falah et al 2019). The slope helps direct the surface runoff velocity and thus influence a flash flood susceptibility (Fenton 2019).…”

Section: Slopementioning

confidence: 99%

“…On the other hand regions with greater slope and altitude are marked classes 1 and 2, and regions with less slope and altitude are categorized as 4 and 5 ( Figure 2), because when the rainfall occurs and with high intensity during a short period of time, the water goes under briskly in very inclined way and in the flat regions. In addition, regions with less steep and flat areas, the water goes sluggishly, this can consequently cause flash flooding (Bannari et al 2016;Falah et al 2019;Fenton 2019).…”

Section: Variable Classification Of Thematic Mapmentioning

confidence: 99%

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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

Talha

Maanan

Atika

et al. 2019

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

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Abstract. In recent decades, many of the countries around the world as well as the south-western Morocco (Guelmim region, Assaka watershed), was subject to flood-storm causing huge human and material damages. The current study focuses on the Prediction of flash flood susceptibility using Fuzzy Analytical Hierarchy Process (FAHP) algorithms and Geographic Information System (GIS) technical. Flash floods areas were identified based on seven flash flood conditioning factors (Soil Moisture Index (SMI), Drainage Density, Rainfall, LULC, Altitude, Slope and Soil). Using AHP the weight derived for the factors were SMI 37% Rainfall 24.30%, Drainage Density 15.57%, LULC 9.98% Altitude 6.39% Slope of the river basin 4.06% and Soil type 2.70%. Then, applying a fuzzy inference system to create flash flood vulnerability maps. The resulting maps were classified into three categories: low, moderate and high flash flood susceptibility; indicated that the areas at the outlet of the watershed and which are close of the main affluent wadis (Seyyad and Oum Al-Achar) were very susceptible to flooding. This study will be helping these zones to be prioritized for the conservation and managing of flash floods.

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Section: Slopementioning

confidence: 99%

Section: Variable Classification Of Thematic Mapmentioning

confidence: 99%

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

Maanan

Atika

et al. 2019

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

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“…Machine learning techniques have contributed significantly to studies of numerous natural hazards, such as landslides [26][27][28][29][30], floods [31][32][33][34][35][36], gully erosion [37][38][39][40], land subsidence [41][42][43][44][45] and debris flows [46][47][48].…”

Section: Introductionmentioning

confidence: 99%

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

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Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

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“…Because of lack of reasonable and trustworthy techniques, most spatial modeling studies reported in the literature use a simple random method to select absence samples in different fields, for example, groundwater potential [3,5,10,[13][14][15][16], landslide susceptibility [1,[17][18][19][20][21], gully erosion susceptibility [22][23][24][25], land subsidence susceptibility [26][27][28][29][30][31], and flood susceptibility [18,[32][33][34][35][36][37]. However, the random sampling method has some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping

et al. 2019

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Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine absence (non-occurrence) samples. In this study, an automated, user-friendly geographic information system (GIS)-based tool, selection of absence samples (SAS), was developed using the Python programming language. The SAS tool takes into account different geospatial concepts, including nearest neighbor (NN) and hotspot analyses. In a case study, it was successfully applied to the Bojnourd watershed, Iran, together with two machine learning models (random forest (RF) and multivariate adaptive regression splines (MARS)) with GIS and remotely sensed data, to model groundwater potential. Different evaluation criteria (area under the receiver operating characteristic curve (AUC-ROC), true skill statistic (TSS), efficiency (E), false positive rate (FPR), true positive rate (TPR), true negative rate (TNR), and false negative rate (FNR)) were used to scrutinize model performance. Two absence sample types were produced, based on a simple random method and the SAS tool, and used in the models. The results demonstrated that both RF (AUC-ROC = 0.913, TSS = 0.72, E = 0.926) and MARS (AUC-ROC = 0.889, TSS = 0.705, E = 0.90) performed better when using absence samples generated by the SAS tool, indicating that this tool is capable of producing trustworthy absence samples to improve groundwater potential models.

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Artificial Neural Networks for Flood Susceptibility Mapping in Data-Scarce Urban Areas

Cited by 114 publications

References 48 publications

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

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

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping

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