Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods

Tang, Xianzhe; Li, Jiufeng; Liu, Minnan; Liu, Wei; Hong, Haoyuan

doi:10.1016/j.catena.2020.104536

Cited by 85 publications

(26 citation statements)

References 67 publications

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“…Researchers have determined that rainfall, topography, and soil significantly influence the occurrence of floods [59], which is inline with our results. Flood susceptibility mapping can be improved using modern techniques, such as the naïve Bayes method of alternating decision tree (AD Tree) and random forest (RF) [60,61] and frequency ratio and support vector machine models [62,63].…”

Section: Discussionmentioning

confidence: 99%

Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud

Swain

Singha

Nayak

2020

IJGI

139

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Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic information system (GIS)/remote sensing (RS) with a cloud computing API on the Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric, permeability, land cover dynamics, and anthropogenic interference, which further had 21 sub-criteria. The relative importance of each criterion prioritized as per their contribution toward flood susceptibility and weightage was given by an AHP pair-wise comparison matrix (PCM). The most and least prominent flood-causing criteria were hydrologic (0.497) and anthropogenic interference (0.037), respectively. An area of ~3000 sq km (40.36%) was concentrated in high to very high flood susceptibility zones that were in the vicinity of rivers, whereas an area of ~1000 sq km (12%) had very low flood susceptibility. The GIS-AHP technique provided useful insights for flood zone mapping when a higher number of parameters were used in GEE. The majorities of detected flood susceptible areas were flooded during the 2019 floods and were mostly located within 500 m of the rivers’ paths.

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Section: Discussionmentioning

confidence: 99%

Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud

Swain

Singha

Nayak

2020

IJGI

139

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“…While the flood locations will be the dependent variable in estimating the flood susceptibility, a number of 12 flood predictors will be used as explanatory variables, and their spatial distribution will be based on the flood exposure values. It is worth stating that the following predictors were selected following a meticulous analysis of the literature (Nguyen et al 2017;Tang et al 2020;Costache et al 2020c): slope, altitude, aspect, TPI, TWI, convergence index, plan curvature, hydrological soil groups, land use, distance from rivers, lithology and rainfall. The first 7 mentioned flood predictors, which are also morphometric indices, were derived from the Digital Elevation Model (DEM) extract from the Shuttle Radar Topographic Mission (SRTM) 30 m. It is worth noting that, at the present moment, for perimeter covered the study area, another DEM with a high resolution of 30 m is not available.…”

Section: Flood Conditioning Factorsmentioning

confidence: 99%

“…Along with its high support for flood and flash-flood early warning systems, the high-accurate estimation of flood exposed areas can also help to draw up the river basins flood defence plans (Albano et al 2017;Johann and Leismann 2017;Brillinger et al 2020). The following models are among the most popular ML algorithms used to estimate the flood susceptibility: Artificial Neural Network (ANN) (Costache et al 2020b), support vector machine (SVM) (Nguyen et al 2017;Tehrany et al 2019;Sahana et al 2020), decision trees-based models (Chen et al 2018;Khosravi et al 2018;Costache 2019c), naïve Bayes (Ali et al 2020;Tang et al 2020), deep learning neural network (Costache et al 2020a), extreme learning machine (Tang et al 2020). All the models applied in the above works achieved an accuracy higher than 80%.…”

Section: Introductionmentioning

confidence: 99%

Detection of areas prone to flood risk using state-of-the-art machine learning models

Costache

Arabameri

Elkhrachy

et al. 2021

Geomatics, Natural Hazards and Risk

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The present study aims to evaluate the susceptibility to floods in the river basin of Buzau in Romania through the following 6 machine learning models: Support Vector Machine (SVM), J48 decision tree, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), Artificial Neural Network (ANN) and Alternating Decision Tree (ADT). In the first stage of the study, an inventory of the areas affected by floods was made in the study area, and a number of 205 flood points were identified. Further, 12 flood predictors were selected to be used for final susceptibility mapping. The six models' training was performed by using 70% of the total flood points that have been associated with the values of flood predictors. The highest accuracy (0.973) was obtained by the RF model, while J48 had the lowest performance (0.825). Besides, by classifying flood predictors' values in flood and non-flood pixels, the six flood susceptibility maps were made. High and very high flood susceptibility values cover between 17.71% (MLP) and 27.93% (ANFIS) of the study area. The validation of the results, performed using the ROC Curve, shows that the most accurate flood susceptibility values are also assigned to the RF model (AUC ¼ 0.996).

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“…Despite this, the existing studies have analyzed the susceptibility of landslides using a single time-related data source without considering whether the date of the data source is consistent with the study date [57,58]. Therefore, it is necessary to understand the temporal evolution of influencing factors (e.g., topography) and the influencing mechanisms of the factors on the susceptibility assessment [59,60]. The changes in influencing factors are significant in earthquake-and landslide-prone areas [61], such as the study area.…”

Section: Advantages and Limitationsmentioning

confidence: 99%

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

Wang

Han

et al. 2020

Applied Sciences

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Digital elevation models (DEMs) are fundamental data models used for susceptibility assessment of landslides. Due to landscape change and reshaping processes, a DEM can show obvious temporal variation and has a significant influence on assessment results. To explore the impact of DEM temporal variation on hazard susceptibility, the southern area of Sichuan province in China is selected as a study area. Multitemporal DEM data spanning over 17 years are collected and the topographic variation of the landscape in this area is investigated. Multitemporal susceptibility maps of landslides are subsequently generated using the widely accepted logistic regression model (LRM). A positive correlation between the topographic variation and landslide susceptibility that was supported by previous studies is quantitatively verified. The ratio of the number of landslides to the susceptibility level areas (RNA) in which the hazards occur is introduced. The RNA demonstrates a general decrease in the susceptibility level from 2000 to 2009, while the ratio of the decreased level is more than fifteen times greater than that of the ratio of the increased level. The impact of the multitemporal DEM on susceptibility mapping is demonstrated to be significant. As such, susceptibility assessments should use DEM data at the time of study.

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Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods

Cited by 85 publications

References 67 publications

Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud

Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud

Detection of areas prone to flood risk using state-of-the-art machine learning models

Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides

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