Assessing physical vulnerability for multi-hazards using an indicator-based methodology

Globally, many built-up areas are threatened by multiple hazards which pose significant threat to humans, buildings and infrastructure. However, the analysis of the physical vulnerability towards multiple hazards is a field that still receives little attention although vulnerability analysis and assessment can contribute significantly to risk reduction efforts. Indicator-based vulnerability approaches are flexible and can be adjusted to the different hazards as well as to specific user needs. In this paper, an indicator-based vulnerability approach, the PTVA (Papathoma Tsunami Vulnerability Assessment), was further developed to be applicable in a multi-hazard context. The resulting multi-hazard version of the PTVA consists of four steps: the identification of the study area and relevant hazards as well as the acquisition of hazard information, the determination of vulnerability indicators and collection of data, the weighting of factors and vulnerability assessment and finally, the consideration of hazard interactions. After the introduction of the newly developed methodology a pilot application is carried out in the Faucon municipality located in the Barcelonnette basin, Southern French Alps. In this case study the vulnerability of buildings to debris flows, shallow landslides and river flooding for emergency planning and for general risk reduction purposes is assessed. The implementation of the methodology leads to reasonable results indicating the vulnerable buildings and supporting the priority setting of different end-users according to their objectives. The constraints of the presented methodology are: a) the fact that the method is not hazard-intensity specific, thus, vulnerability is measured in a rather qualitative and relative way and b) the high amount of data required for its performance. However, the advantage is that it is a flexible method which can be applied for the vulnerability analysis in a multi-hazard context but also it can be adjusted to the user-specific needs to support decision-making.     

Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.     

A comparison of selected global disaster risk assessment results

We compare country risk rankings derived from two recently published global disaster risk analyses. One set of country rankings is based on the Disaster Risk Index (DRI) developed by the United Nations Environment Program (UNEP) Division of Early Warning and Assessment Global Resource Information Database project under a contract to the United Nations Development Program (UNDP). The other is based on an index of disaster mortality risk developed by the Global Natural Disaster Risk Hotspots project implemented by Columbia University, the World Bank and associated partners. We convert data from these sources into two comparable indexes of disaster mortality risk and rank countries according to the resulting values for a set of natural hazards common to both studies. The country rankings are moderately correlated, ranging from .41 to .56 for individual hazards to .31 for multi-hazard mortality risks. We identify the top 25 countries according to the mortality risk values we recomputed from each study’s results to show the degree to which countries are highly ranked in common. The numbers of countries common to both lists for individual hazards range from 7 to 16 out of 25. The correspondence among the top 25 ranked countries is lowest for earthquakes and floods. Only 6 out of 25 countries are common to both lists in the multi-hazard case. We suggest that while the convergence in the results for some hazards is encouraging, more work is needed to improve data and methods, particularly with respect to assessing the role of vulnerability in the creation of risk and the calculation of multi-hazard risks. The views expressed are the authors’ and do not necessarily reflect those of SM2 Consulting Multi-Hazards and Risk Holistic Solutions or the United Nations Development Program.     

Empirical evidence of declining global vulnerability to climate-related hazards

Death tolls and economic losses from natural hazards continue to rise in many parts of the world. With the aim to reduce future impacts from natural disasters it is crucial to understand the variability in space and time of the vulnerability of people and economic assets. In this paper we quantified the temporal dynamics of socio-economic vulnerability, expressed as fatalities over exposed population and losses over exposed GDP, to climate-related hazards between 1980 and 2016. Using a global, spatially explicit framework that integrates population and economic dynamics with one of the most complete natural disaster loss databases we quantified mortality and loss rates across income levels and analyzed their relationship with wealth. Results show a clear decreasing trend in both human and economic vulnerability, with global average mortality and economic loss rates that have dropped by 6.5 and nearly 5 times, respectively, from 1980–1989 to 2007–2016. We further show a clear negative relation between vulnerability and wealth, which is strongest at the lowest income levels. This has led to a convergence in vulnerability between higher and lower income countries. Yet, there is still a considerable climate hazard vulnerability gap between poorer and richer countries.     

基于指标法的建筑物理脆弱性评估研究进展

承灾体脆弱性评估是科学进行灾害风险评估和预测的基础，房屋建筑作为面大量广的承灾体，众多学者对建筑物理脆弱性指标模型进行了研究。基于单灾种和多灾种2个维度，针对指标模型构建的各环节，全面梳理了几种典型单灾种物理脆弱性指标体系和评估模型构建情况，发现指标选取理论依据不明确，模型构建主观性较强，不能准确表征建筑特点与抗灾能力间的内在联系。系统总结了多灾种指标体系和耦合物理脆弱性指标模型研究现状，发现多灾种之间及其对承灾体影响的复杂耦合效应在现有指标模型中未得到充分体现。研究结果表明，明晰指标依据、优化模型构建是提升单灾种物理脆弱性评估准确性的关键；改进脆弱性耦合模型、拓展综合脆弱性评估方法是健全多灾种脆弱性评估研究的核心。     

Multi-hazard risk mapping and assessment on an active volcano: the GRINP project at Mount Cameroon

To help improve the safety of its population faced with natural disasters, the Cameroon Government, with the support of the French Government, initiated a programme of geological risk analysis and mapping on Mount Cameroon. This active volcano is subject to a variety of hazards: volcanic eruptions, slope instability and earthquakes. Approximately 450,000 people live or work around this volcano, in an area which includes one of Cameroon’s main economic resources. An original methodology was used for obtaining the information to reply to questions raised by the authorities. It involves several stages: identifying the different geological hazard components, defining each phenomenon’s threat matrix by crossing intensity and frequency indices, mapping the hazards, listing and mapping the exposed elements, analysing their respective values in economic, functional and strategic terms, establishing typologies for the different element-at-risk groups and assessing their vulnerability to the various physical pressures produced by the hazard phenomena, and establishing risk maps for each of the major element-at-risk groups (population, infrastructures, vegetation, atmosphere). At the end of the study we were able (a) to identify the main critical points within the area, and (b) provide quantified orders of magnitude concerning the dimensions of the risk by producing a plausible eruption scenario. The results allowed us to put forward a number of recommendations to the Cameroon Government concerning risk prevention and management. The adopted approach corresponds to a first level of response to the authorities. Later developments should make it possible to refine the quality of the methodology.     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.