Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning

Costache, Romulus; Popa, Mihnea Cristian; Bui, Dieu Tien; Diaconu, Daniel Constantin; Ciubotaru, Nicu; Minea, Gabriel; Pham, Quoc Bao

doi:10.1016/j.jhydrol.2020.124808

Cited by 89 publications

(38 citation statements)

References 68 publications

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“…The combined methodologies use a mix of remote sensing and GIS techniques with those of modeling and simulation, statistics, or integration of machine learning algorithms. Recent years have shown us a diversification in the methodological approaches proposed by researchers, so that more and more research using methodologies based on machine learning (2% of articles) [100][101][102][103] and comparative methods (2% of total papers) [16,17,[104][105][106] is starting to be published. The use of combined methods is also gaining more and more interest from researchers.…”

Section: Research Methods and Advances In Flood Researchmentioning

confidence: 99%

“…The development of data processing techniques in a large volume and the use of a large number of parameters has allowed for the development of new methodological approaches of high precision to mitigate the effects caused by floods. The spatialization of these methodologies using GIS techniques has opened the way for researchers to develop or improve existing methods but also to combine algorithms, thus leading to the creation of hybrid algorithms [17,105,106].…”

Section: Research Methods and Advances In Flood Researchmentioning

confidence: 99%

“…However, some risk assessment research assessing the potential damage caused by floods produces flood maps combined with social and economic spatial data. This economic assessment of losses involves many parameters, some very subjective, that interact in a complex and non-linear way [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Overview of Flood Risk Analysis Methods

Diaconu

Costache

Popa

2021

Water

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Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies.

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Section: Research Methods and Advances In Flood Researchmentioning

confidence: 99%

Section: Research Methods and Advances In Flood Researchmentioning

confidence: 99%

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An Overview of Flood Risk Analysis Methods

Diaconu

Costache

Popa

2021

Water

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“…Kernel function is a one of the most fashionable which called Radial Basic Function (RBF) was applied to address non-linearity of the classification (Pourghasemi et al 2013;Costache et al 2020b). For transforming the nonlinear classes into a linear one in high dimensional space was used (Marjanovi c et al 2011; Poeppl et al 2017) following the Equation 6:…”

Section: Multi-collinearity Analysismentioning

confidence: 99%

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Pham

Mukherjee²,

Norouzi

et al. 2020

Geomatics, Natural Hazards and Risk

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“…A wide range of attempts have been made to map flash flooding using various artificial intelligence techniques optimized by metaheuristic algorithms for flooding capacity [17,18]. More recent studies used different machine learning algorithms in predicting and zoning the flash flooding areas [19][20][21]. However, only a few studies integrated remotely sensed data and spatial data in machine learning techniques for improving the accuracy of spatial prediction of flash floods, despite the fact that air-borne remote sensing data provide a number of benefits such as easier repeatability, low cost, and wider area coverage [21,22], resulting in a lack of cost-effective, precise, and timely models for the susceptibility mapping of flash floods.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

et al. 2020

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Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

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Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning

Cited by 89 publications

References 68 publications

An Overview of Flood Risk Analysis Methods

An Overview of Flood Risk Analysis Methods

Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

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