Classifiers for the detection of flood-prone areas using remote sensed elevation data

Degiorgis, Massimiliano; Gnecco, Giorgio; Gorni, Silvia; Roth, Giorgio; Sanguineti, Marcello; Taramasso, Angela Celeste

doi:10.1016/j.jhydrol.2012.09.006

Cited by 119 publications

(76 citation statements)

References 27 publications

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“…In this framework, the research has recently shown that the delineation of flood-prone areas can be carried out using simplified methods that rely on basin geomorphologic features (e.g., Nardi et al 2006;Manfreda et al 2011Manfreda et al , 2014aDegiorgis et al 2012;Jalayer et al 2014;De Risi et al 2014;Papaioannou et al 2014). Such innovative procedures may provide a preliminary delineation of the flood-prone areas useful for the planning of numerical analyses, and for insurance companies that have a growing interest toward the identification of the assets and population at risk.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, Degiorgis et al (2012) introduced the use of linear binary classifiers to investigate the relationships between several morphological features and the flooding hazard at the catchment scale. In the present work, we extend the number of morphological features investigated, using single local features as well as composite indices (built with the specific aim to represent a metric of flood hazard) derived from digital elevation models (DEMs).…”

Section: Introductionmentioning

confidence: 99%

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Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models

et al. 2015

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The identification of flood-prone areas is a critical issue becoming everyday more pressing for our society. A preliminary delineation can be carried out by DEM-based procedures that rely on basin geomorphologic features. In the present paper, we investigated the dominant topographic controls for the flood exposure using techniques of pattern classification through linear binary classifiers based on DEM-derived morphologic features. Our findings may help the definition of new strategies for the delineation of floodprone areas with DEM-based procedures. With this aim, local features-which are generally used to describe the hydrological characteristics of a basin-and composite morphological indices are taken into account in order to identify the most significant one. Analyses are carried out on two different datasets: one based on flood simulations obtained with a 1D hydraulic model, and the second one obtained with a 2D hydraulic model. The analyses highlight the potential of each morphological descriptor for the identification of the extent of flood-prone areas and, in particular, the ability of one geomorphologic index to represent flood-inundated areas at different scales of application.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models

et al. 2015

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“…The research of Townsend and Walsh (1998) has been a pioneer landmark in demonstrating the potential of RS modelling in flood prediction in a GIS environment. Youssef et al (2011), Pradhan (2010a, Pradhan et al (2009), Garcıa-Pintado et al (2013), Stephens et al (2012), Prakash et al (2012), Degiorgis et al (2012), Hostache et al (2010) and others have since used this combined approach. Some results from these studies have been successful, while others have been flawed (Matgen et al 2007).…”

Section: Introductionmentioning

confidence: 99%

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany

Shabani

Jebur

et al. 2017

Geomatics, Natural Hazards and Risk

221

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The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.

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“…This index allows for the delineation of a portion of hydrographic basin potentially exposed to flood inundation, by identifying all the areas characterized by a TWI that exceeds a given threshold; this threshold is strictly dependent on the geomorphology of the hydrographic basin. The threshold value can be calibrated based on results of detailed delineation of the inundation profile for selected zones (Motevalli and Vafakhah 2016;Jalayer et al 2014;Degiorgis et al 2012;Manfreda et al 2008Manfreda et al , 2014Manfreda et al , 2015. High exposure areas are identified by using a map of urban morphology types (UMTs), from which residential UMTs are extracted, and a geo-spatial census dataset for demographic information (e.g.…”

Section: Introductionmentioning

confidence: 99%

Delineation of flooding risk hotspots based on digital elevation model, calculated and historical flooding extents: the case of Ouagadougou

Risi

Jalayer

Paola

et al. 2017

Stoch Environ Res Risk Assess

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Delineation of flood risk hotspots can be considered as one of the first steps in an integrated methodology for urban flood risk management and mitigation. This paper presents a step-by-step methodology in a GISbased framework for identifying flooding risk hotspots for residential buildings. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. The novelty of this paper consists in the fact that the floodprone areas (the TWI thresholds) are identified through a maximum likelihood method (MLE) based both on inundation profiles calculated for a specific return period (T R ), and on information about the extent of historical flooding in the area of interest. Furthermore, Bayesian parameter updating is employed in order to estimate the TWI threshold by employing the historical extent as prior information and the inundation map for calculating the likelihood function. For different statistics of the TWI threshold, the map of potentially flood-prone areas is overlaid with the map of residential urban morphology units in order to delineate the residential flooding risk urban hotspots. Overlaying the delineated urban hotspots with geo-spatial census datasets, the number of people affected by flooding is estimated. These kind of screening procedures are particularly useful for locations where there is a lack of detailed data or where it is difficult to perform accurate flood risk assessment. In fact, an application of the proposed procedure is demonstrated for the identification of urban flooding risk hotspots in the city of Ouagadougou, capital of Burkina Faso, a city for which the observed spatial extent of a major flood event in 2009 and a calculated inundation map for a return period of 300 years are both available.

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Classifiers for the detection of flood-prone areas using remote sensed elevation data

Cited by 119 publications

References 27 publications

Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models

Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Delineation of flooding risk hotspots based on digital elevation model, calculated and historical flooding extents: the case of Ouagadougou

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