Urban flood disaster risk evaluation based on ontology and Bayesian Network

Wu, Zening; Shen, Yanxia; Wang, Shuangfeng; Wu, Manman

doi:10.1016/j.jhydrol.2020.124596

Cited by 72 publications

(23 citation statements)

References 27 publications

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“…Rivers can provide wet and saturated water of the sliding areas, which may reduce the shear strength of the soil and weak layer, and reduce the stability of a highway slope, so distance to rivers is usually considered as an important impact factor of LSM [ 20 , 40 ].…”

Section: Susceptibility Evaluation Indexes Of Hldsmentioning

confidence: 99%

“…Researches on landslide susceptibility mapping (LSM) in China mainly focused on the Wenchuan, Yushu and Ya’an earthquake areas, the Three Gorges Reservoir areas, the areas affected by typhoons and loess areas; researches abroad China mainly focused on the Medellin areas (Columbia), Kyushu areas (Japan) and some areas in Italy [ 13 – 15 ]. The modeling methods implemented to LSM mainly included the statistical prediction models, i.e., Logistic regression method (LR), decision tree method, analytical hierarchy process (AHP), deterministic coefficient method and multivariate adaptive regression spline model (MARSplines), and the machine learning models, i.e., artificial neural network (ANN), support vector machine (SVM), neuro-fuzzy technique, decision tree model and Bayesian network (BN), some scholars also conducted comparison researches on multiple modeling methods [ 11 , 16 – 20 ]. Representative studies included: Wang et al [ 21 ] used the LR, bivariate statistical analysis (BS) and MARSplines to create landslide susceptibility maps by comparing the past landslide distribution and conditioning factor thematic maps; Alireza et al [ 22 ] proposed a novel hybrid model based on the step-wise weight evaluation ratio analysis (SWARA) method and adaptive neuro-fuzzy inference system (ANFIS) to evaluate landslide susceptible areas using geographical information system (GIS); Zhang et al [ 23 ] used the information value model and LR to build the susceptibility evaluation systems based on the data of 655 landslides in the history of Wanzhou district (Chongqing); Sezer et al [ 24 ] conducted landslide susceptibility evaluation by applying the methods of M-AHP and Mamdani type FIS by using the expert-based LSM module; Chen et al [ 25 ] built a landslide susceptibility model using three well-known machine learning models namely the maximum entropy (MaxEnt), SVM and ANN, and accompanied by their ensembles (i.e., ANN-SVM, ANN-MaxEnt, ANN-MaxEnt-SVM and SVM-MaxEnt) in Wanyuan (China); Zhu et al [ 26 ] developed and compared two presence-only methods including the one-class SVM and kernel density estimation (KDE), and two presence-absence methods including the ANN and two-class SVM to evaluate their respective performance in mapping landslide susceptibility; Chen et al [ 11 ] assessed and compared four advanced machine learning techniques, namely the BN, radical basis function classifier (RBF), logistic model tree (LMT) and random forest (RF) models, for landslide susceptibility modeling in Chongren, China; Yang et al [ 27 ] proposed a new LSM method based on the GeoDetector and spatial logistic regression model (SLR), of which, the GeoDetector was used to select condition factors based on the spatial distribution of landslides, SLR model was used to make full use of the structural and attribute information of spatial objects simultaneously in LSM.…”

Section: Introductionmentioning

confidence: 99%

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Susceptibility mapping and zoning of highway landslide disasters in China

Yin

Li²,

Che

et al. 2020

PLoS ONE

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Prominent regional differentiations of highway landslide disasters (HLDs) bring great difficulties in highway planning, designing and disaster mitigation, therefore, a comprehensive understanding of HLDs from the spatial perspective is a basis for reducing damages. Statistical prediction methods and machine learning methods have some defects in landslide susceptibility mapping (LSM), meanwhile, hybrid methods have been developed by combining the statistical prediction methods with machine learning methods in recent years, and some of them were reported to perform better than conventional methods. In view of this, the principal component analysis (PCA) method was used to extract the susceptibility evaluation indexes of HLDs; the particle swarm optimization-support vector machine (PSO-SVM) model and genetic algorithm-support vector machine (GA-SVM) model were implemented to the susceptibility mapping and zoning of HLDs in China. The research results show that the accumulative contribution rate of the four principal components is 92.050%; evaluation results of the PSO-SVM model are better than those of the GA-SVM model; micro dangerous areas, moderate dangerous areas, severe dangerous areas and extreme dangerous areas account for 24.24%, 19.49%, 36.53% and 19.74% of the total areas of China; among the 1543 disaster points in the HLDs inventory, there are 134, 182, 421 and 806 located in the above areas respectively.

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Section: Susceptibility Evaluation Indexes Of Hldsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Susceptibility mapping and zoning of highway landslide disasters in China

Yin

Li²,

Che

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Different methods of disaster management are applicable at three stages of the event. First is the pre-disaster stage, which emphasises monitoring or early warning system to alert the authorities about the incoming natural event; second is damage control during the event, and third is the post-disaster recovery phase to bring life back to normality [6][7][8]. To address the challenges of natural events, the International Emergency Management System (IEMS) was established in 1993 to set up procedures and guidelines for countries to adapt during a crisis scenario.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

et al. 2021

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Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.

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“…But the reality is that the relationship among influencing factors of urban flood inundation is complex and uncertain, and the influencing factors are interdependent, which directly or indirectly affect the occurrence of flood inundation. BN further analyzes the relationship between influencing factors of urban flood inundation [28,29]. BN is a popular approach to estimate uncertainty in risk evaluation in terms of the likelihood of disasters [30], which can quantify uncertainty and capture the potential relationship among risk factors to evaluate urban flood inundation risk objectively [31,32].…”

Section: Introductionmentioning

confidence: 99%

Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network

Huang

Wang

et al. 2021

Land

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Drivers of urban flood disaster risk may be related to many factors from nature and society. However, it is unclear how these factors affect each other and how they ultimately affect the risk. From the perspective of risk uncertainty, flood inundation risk is considered to be the probability of inundation consequences under the influence of various factors. In this paper, urban flood inundation risk assessment model is established based on Bayesian network, and then key disaster-causing factors chains are explored through influence strength analysis. Jingdezhen City is selected as study area, where the flood inundation probability is calculated, and the paths of these influential factors are found. The results show that the probability of inundation in most areas is low. Risk greater than 0.8 account for about 9%, and most of these areas are located in the middle and southern section of the city. The influencing factors interact with each other in the form of factor chain and, finally, affect the flood inundation. Rainfall directly affects inundation, while river is the key factor on inundation which is influenced by elevation and slope. In addition, in the chain of socio-economic factors, the population will determine the pipe density through affecting gross domestic product (GDP), and lead to the inundation. The approach proposed in this study can be used to find key disaster-causing factors chains, which not only quantitatively reveal the formation of risks but also provide reference for early warning.

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Urban flood disaster risk evaluation based on ontology and Bayesian Network

Cited by 72 publications

References 27 publications

Susceptibility mapping and zoning of highway landslide disasters in China

Susceptibility mapping and zoning of highway landslide disasters in China

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network

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