An ontology-based framework for heterogeneous data management and its application for urban flood disasters

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

doi:10.1007/s12145-019-00439-3

Cited by 26 publications

(22 citation statements)

References 29 publications

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“…According to the experts' knowledge and relevant literature, the influencing factors of urban flood inundation are identified and presented as the nodes of BN, type and state value of each factors are determined based on the nature of factors. Through field investigation and historical research, the relationships among the influencing factors are determined [41], and they are synthesized to construct the directed acyclic graph (DAG) of BN. (2) The data collection and pre-processing of the study area are carried out (Section 3).…”

Section: Framework Of Research Methodsmentioning

confidence: 99%

“…Ten factors are selected to evaluate urban flood inundation risk and determine inundation-prone areas in the study area according to previous studies [1,2,11,14,[32][33][34]41,44], the actual situation of the study area, the availability of data, as well as experts' knowledge and experience. The hazard factor is annual rainfall; the vulnerability factors are elevation, slope, soil water retention (SWR), river density, distance to river; the capacity factors are pipe density, road density, population density, and per unit GDP (Figure 2).…”

Section: Selection Of Criteria and Risk Factorsmentioning

confidence: 99%

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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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Section: Framework Of Research Methodsmentioning

confidence: 99%

Section: Selection Of Criteria and Risk Factorsmentioning

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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“…Over the years, rainfall forecast and satellite images have been used for predicting and monitoring flooding conditions. A wide range of examples also exists, primarily in the modelling of natural hazards and emergencies [31] or the wider context of flooding [32]. Methods such as Liquid level monitoring [33], the water level of gully pot monitoring [34,35] has been applied.…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

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“…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. According to the Millenium Development Goals (MDG) 2015, the latest information technology and communication tools can be used for improving the relief response during a natural disaster [9][10][11][12]. The relief efforts can be accelerated to reach out to a maximum number of people in a short time with the use of advanced technology.…”

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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An ontology-based framework for heterogeneous data management and its application for urban flood disasters

Cited by 26 publications

References 29 publications

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

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

Explainable Artificial Intelligence for Developing Smart Cities Solutions

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

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