Supervised and semi-supervised classifiers for the detection of flood-prone areas

Gnecco, Giorgio; Morisi, Rita; Roth, Giorgio; Sanguineti, Marcello; Taramasso, Angela Celeste

doi:10.1007/s00500-015-1983-z

Cited by 21 publications

(13 citation statements)

References 18 publications

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“…Subsequently, a total number of 1866 points representing flash flood locations were divided into two datasets: 70% of the locations were randomly selected and used as the training set, and the remaining 30% of locations were used as the testing set to validate the model accuracy, as suggested in [56,[89][90][91]. Finally, a sampling process was performed to generate values of the ten influencing factors.…”

Section: Flash-flood Database Establishment Coding and Checkingmentioning

confidence: 99%

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

et al. 2020

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Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.

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Section: Flash-flood Database Establishment Coding and Checkingmentioning

confidence: 99%

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

et al. 2020

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“…Several other approaches have been used for mapping flood susceptibility [21]. Methods relying on geomorphologic characteristics of a basin have been used in several studies [22][23][24][25]. However, it should be noted that these methods cannot substitute traditional hydraulic modeling [26,27], but they could be used, especially, in large-scale analysis or in developing countries [28].…”

Section: Introductionmentioning

confidence: 99%

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

Vojtek

Vojteková

2019

Water

216

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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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“…Commonly, Machine Learning (ML; Breiman, 1984) models are used, often ensembled with Multi-Criteria Decision-Making (MCDM) techniques (Triantaphyllou et al, 2000;Ho et al, 2010). Some authors (Degiorgis et al, 2012;Gnecco et al, 2017) have tested a blend of GDs, while some others mixed these indices with information on land use, soil geology and climate, and compared different combination strategies (e.g., Wang et al, 2015;Lee et al, 2017;Khosravi et al, 2018;Arabameri et al, 2019;Janizadeh et al, 2019;Costache et al, 2020). These studies suggest that data-driven flood hazard mapping has a remarkable potential.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains

Magnini

Lombardi

Persiano

et al. 2021

Preprint

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Abstract. Recent literature shows several examples of simplified approaches that perform flood hazard (FH) assessment and mapping across large geographical areas on the basis of fast-computing geomorphic descriptors. These approaches may consider a single index (univariate) or use a set of indices simultaneously (multivariate). What is the potential and accuracy of multivariate approaches relative to univariate ones? Can we effectively use these methods for extrapolation purposes, i.e. FH assessment outside the region used for setting up the model? Our study addresses these open problems by considering two separate issues: (1) mapping flood-prone areas, and (2) predicting the expected water depth for a given inundation scenario. We blend seven geomorphic descriptors through Decision Tree models trained on target FH maps, referring to a large study area (≈105 km2). We discuss the potential of multivariate approaches relative to the performance of a selected univariate model and on the basis of multiple extrapolation experiments, where models are tested outside their training region. Our results show that multivariate approaches may (a) significantly enhance flood-prone area delineation (overall accuracy: 93 %) relative to univariate ones (overall accuracy: 84 %), (b) provide accurate predictions of expected inundation depths (determination coefficient ≈0.7), and (c) produce encouraging results in extrapolation.

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Supervised and semi-supervised classifiers for the detection of flood-prone areas

Cited by 21 publications

References 18 publications

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

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

Machine-Learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains

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