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Figure 3 | Conditioning factors incorporated within the vulnerability as GIS layers: (a) building density, (b) building age (c), population density, and (d) socio-economic conditions density is important because it has significant impacts on the damage caused by urban floods. It was divided into four classes: high (>300 dwellings per hectare), medium (200-300 dwellings per hectare), low (100-200 dwellings per hectare), and very low (<100 dwellings per hectare) (Figure 3(a)). Building age was divided into five classes: recently completed (<5 years), new (10-19 years), medium (20-29 years), old (30-39 years), and very old (>40 years) (Figure 3(b)). The population density refers to the number of people inhabiting a given urbanized area, where high levels reflect the population at risk to floods (Giineralp et al. 2017). It was divided into four classes: high (>1,500/km?), medium (1,500-1,000/km?), low (1,000-500/km?), and very low (<500/km?) (Figure 3(c)). Socio-economic conditions refer to the inherent properties and behavior of humans and society within a specific urbanized region, and are assessed Aratijo 2016; Darabi et al. 2019) to relate response variables

Figure 3 | Conditioning factors incorporated within the vulnerability as GIS layers: (a) building density, (b) building age (c), population density, and (d) socio-economic conditions density is important because it has significant impacts on the damage caused by urban floods. It was divided into four classes: high (>300 dwellings per hectare), medium (200-300 dwellings per hectare), low (100-200 dwellings per hectare), and very low (<100 dwellings per hectare) (Figure 3(a)). Building age was divided into five classes: recently completed (<5 years), new (10-19 years), medium (20-29 years), old (30-39 years), and very old (>40 years) (Figure 3(b)). The population density refers to the number of people inhabiting a given urbanized area, where high levels reflect the population at risk to floods (Giineralp et al. 2017). It was divided into four classes: high (>1,500/km?), medium (1,500-1,000/km?), low (1,000-500/km?), and very low (<500/km?) (Figure 3(c)). Socio-economic conditions refer to the inherent properties and behavior of humans and society within a specific urbanized region, and are assessed Aratijo 2016; Darabi et al. 2019) to relate response variables

Related Figures (10)

Figure 1 | (Top) Location of Amol city in northern Iran and the (center) map of the city showing points with a history of flooding and (bottom) previous flooding at (a) the Amol suspension bridge, (b) Banovan park, and (c) Bidestan park.
Figure 1 | (Top) Location of Amol city in northern Iran and the (center) map of the city showing points with a history of flooding and (bottom) previous flooding at (a) the Amol suspension bridge, (b) Banovan park, and (c) Bidestan park.
Figure 2 | Conditioning factors incorporated within the hazard model as GIS layers: (a) rainfall, (b) land cover/land use, (c) digital elevation map (DEM), (d) slope, (e) SCS CN, (f) distance to river, (g) distance to channels, and (h) depth to groundwater.
Figure 2 | Conditioning factors incorporated within the hazard model as GIS layers: (a) rainfall, (b) land cover/land use, (c) digital elevation map (DEM), (d) slope, (e) SCS CN, (f) distance to river, (g) distance to channels, and (h) depth to groundwater.
Table 1 | Validation of the seven machine learning models and the ensemble model using the ROC-AUC method
Table 1 | Validation of the seven machine learning models and the ensemble model using the ROC-AUC method
Figure 4 | Validation of the ensemble model using the ROC-AUC method.
Figure 4 | Validation of the ensemble model using the ROC-AUC method.
“igure 5 | Importance of different conditioning factors in urban flood hazard prediction based on the ensemble model.
“igure 5 | Importance of different conditioning factors in urban flood hazard prediction based on the ensemble model.
Figure 6 | Urban flood hazard probability maps for the study area created using the individual models (a) SVM, (b) RF, (c) MAXENT, (d) BRT, (e) MARS, (f) GLM, and (g) GAM, and (h) the ensemble model.
Figure 6 | Urban flood hazard probability maps for the study area created using the individual models (a) SVM, (b) RF, (c) MAXENT, (d) BRT, (e) MARS, (f) GLM, and (g) GAM, and (h) the ensemble model.
Figure 8 | Spatial distribution of flood risk in Amol city predicted using the ensemble model, depicted as (a) flood risk index and (b) flood risk zones. Figure 7 | Urban flood vulnerability maps for Amol city showing (a) flood vulnerability value and (b) flood vulnerability zones.
Figure 8 | Spatial distribution of flood risk in Amol city predicted using the ensemble model, depicted as (a) flood risk index and (b) flood risk zones. Figure 7 | Urban flood vulnerability maps for Amol city showing (a) flood vulnerability value and (b) flood vulnerability zones.
Table 2 | Weight and rank values assigned to the different classes of the building density, building age, population density, and socio-economic factors
Table 2 | Weight and rank values assigned to the different classes of the building density, building age, population density, and socio-economic factors
Figure 9 | Histogram assessment of the ensemble model, showing flood point occurrence in five different flood risk zones (very low to very high). Urban flooding has been modeled previously using models, such as Mike and LisFlood-FP. However, these hydrological and hydraulic models simulate the physical processes of urban flooding conditions and require sophisti- cated datasets and abundant computations. Thus, in recent years, machine learning algorithms have been widely used in environmental and especially flood risk studies, in which mapping-based models are important. Models for mapping food-prone areas have been used by researchers worldwide and most agree that developing a specific model and identifying appropriate flood conditioning fac- tors are important. In this study, machine learning algorithms were used to identify flood-prone areas in an urban environment, Amol city in Iran. In these models, urban flood potential areas are determined by a flood inven- tory map, which shows the maximum probability of urban flooding occurring in those locations. The identification of flooding occurring in those locations. The identification o
Figure 9 | Histogram assessment of the ensemble model, showing flood point occurrence in five different flood risk zones (very low to very high). Urban flooding has been modeled previously using models, such as Mike and LisFlood-FP. However, these hydrological and hydraulic models simulate the physical processes of urban flooding conditions and require sophisti- cated datasets and abundant computations. Thus, in recent years, machine learning algorithms have been widely used in environmental and especially flood risk studies, in which mapping-based models are important. Models for mapping food-prone areas have been used by researchers worldwide and most agree that developing a specific model and identifying appropriate flood conditioning fac- tors are important. In this study, machine learning algorithms were used to identify flood-prone areas in an urban environment, Amol city in Iran. In these models, urban flood potential areas are determined by a flood inven- tory map, which shows the maximum probability of urban flooding occurring in those locations. The identification of flooding occurring in those locations. The identification o
Related topics:
Civil EngineeringEnvironmental ScienceHydrologyGeospatial AnalysisROC AUCensemble modelFlood Myth
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