Disaster Losses Research Articles

Improving the efficiency of the Vietnamese Police Force of Fire Protection and Rescue at various types of incidents and natural disasters

Purpose. Research of statistics of natural disasters in Vietnam and ways how to minimize human losses by improvement the effectiveness of rescue activities. Methods. Statistical. Findings. Proposals are presented to improve the effectiveness of rescue activities to minimize human losses in incidents and natural disasters. Application field of research. Rescue activities for various types of incidents and natural disasters of the Vietnamese police force of fire protection and rescue.

Study of seismic vulnerability of steel frame structures on soft ground considering group effect

Earthquakes have caused increasingly severe casualties and economic losses in coastal urban areas where soft soils are widely distributed. To mitigate disaster losses, this paper aims to investigate the mechanism of the group effect, the interaction between a group of buildings (at least three) through the foundation, on seismic vulnerability in coastal urban areas. Firstly, the analysis models are established, and the validity is subsequently verified. Meanwhile, a self-built automatic iterative calculation program for multilayer soils is adopted to consider the nonlinearity of the soft soils. On this basis, probabilistic seismic demand analyses are further performed using ten earthquake ground motions. The seismic vulnerability formulas of the structures are obtained in combination with the probabilistic seismic demand models. Finally, the influence of the group effect on the seismic performance of the structures is evaluated from different perspectives based on the vulnerability formulas. The results indicate that the group effect of the steel frame structures makes the performance margin ratio (PMR) significantly higher and reduces the seismic vulnerability of the structures in coastal urban areas. The mechanism revealed in this paper presents a more accurate way to conduct earthquake-resistant design, provides guidelines for setting the criteria, and is likewise instructive to the seismic design of other structural forms.

Flash flood detection and susceptibility mapping in the Monsoon period by integration of optical and radar satellite imagery using an improvement of a sequential ensemble algorithm

Rainfall monsoons and the resulting flooding have always been cataclysmic disasters that have heightened global concerns in light of climate change. Flood susceptibility modeling is an indirect method for reducing flood disaster losses. This study aimed to improve flood susceptibility modeling by developing a sequential ensemble (extreme gradient boosting (XGBoost)) model utilizing three swarm-based algorithms (bacterial foraging optimization (BFO), cuckoo search (CS), and artificial bee colony (ABC) algorithms). Initially, an integration of optical (Landsat-8) and radar (Sentinel-1) satellite images were used to monitor the flooded areas during the July 2022 monsoon in the Kazerun region, Iran. A total of 1358 flood occurrence points were considered from the monitored flood areas; 70% (952 points) and 30% (406 points) were used for modeling and evaluating the models, respectively. Based on flood points and thirteen spatial criteria influencing floods, four flood models ((XGBoost, XGBoost-ABC, XGBoost-BFO, and XGBoost-CS)) were used to generate a flood susceptibility map (FSM). According to the results, the XGBoost-CS (area under the curve (AUC) = 0.96), XGBoost-BFO (AUC = 0.953), XGBoost-ABC (AUC = 0.941), and XGBoost (AUC = 0.939) models have greater accuracy in flood susceptibility modeling, respectively. The results indicated that the models coupled with the three metaheuristic algorithms (XGBoost-ABC, XGBoost-BFO, and XGBoost-CS) exhibited higher flood susceptibility modeling accuracy than the standalone XGBoost model.

Post-Earthquake Scheduling of Rescuers: A Method Considering Multiple Disaster Areas and Rescuer Collaboration

Reasonable and efficient scheduling of rescuers plays a crucial role in earthquake emergency relief, which can effectively reduce disaster losses and promote social stability and sustainable development. Due to the suddenness of disasters, the urgency of time, and the complexity of rescue efforts, scheduling of rescuers often involves multiple disaster areas, multiple departure areas, and diverse rescue tasks. However, most existing studies have paid little attention to the scheduling problem of rescuers considering multiple disaster areas, multiple departure areas, and multiple rescue tasks and have not comprehensively considered the collaboration of rescuers and task requirements. Thus, how to reasonably dispatch rescuers to disaster areas by considering the collaboration of rescuers and task needs is a noteworthy research problem. The objective of this paper is to propose a method considering multiple disaster areas and the collaboration of rescuers to solve the scheduling problem of rescuers after earthquakes. Firstly, the collaborative degrees of rescuers are calculated according to the collaborative performance among rescuers concerning collaborative feature indicators. Secondly, according to the performance of rescuers concerning professional skill evaluation indicators, the professional abilities of rescuers are calculated, and the comprehensive performance indicators for rescuers are obtained by aggregating the collaborative degrees and the professional abilities of rescuers. Thirdly, the time satisfaction degrees are calculated based on the times taken by rescuers from different departure areas to disaster areas and the time requirements of disaster areas. Then, the time satisfaction degrees and the comprehensive performance of rescuers are aggregated to obtain the comprehensive matching degrees. Furthermore, a rescuer scheduling model for earthquake emergency rescue is constructed to maximize the comprehensive matching degrees between rescuers and rescue tasks, and the optimal scheduling scheme is determined by solving the model. Finally, a case study and comparative analyses are presented to verify the rationality and feasibility of the proposed method. The results show that the proposed method can reasonably assign rescuers to quickly respond to the needs of rescue tasks in disaster areas, and is better than the other two methods in terms of rescue comprehensive capability evaluation. The proposed method can provide decision support for solving the post-earthquake scheduling problem of rescuers and help to improve the emergency response ability for large-scale geological disaster events.

Small and Medium-Sized Entrepreneurs’ Perceptions of Flood Loss and Damage in Sri Lanka

As climate change has intensified flood risk and damage in many low-lying areas of the world, small and medium-sized enterprises (SMEs), which typically exist in developing countries, have endured high flood risks without much support for relief. This study investigates how SMEs in flood-prone areas of Colombo, Sri Lanka, the largest business hub in the country, have perceived and dealt with flood loss and damage in the past ten years. We conducted field surveys and a questionnaire survey among 60 SME owners in two flood-prone administrative units from March to June 2020. The results show that informal businesses experienced more flood loss and damage than other community members. Also, the community dominated by informal businesses tended to be located closer to potential flood sources. Ownership and awareness about flood insurance were very low in our study areas. Temporary business closure was the most serious loss experienced by informal business communities. These communities depended on personal savings to recover from floods. Our multiple regression analysis found that age, education, and experience significantly influenced SME owners’ perceptions and experiences about floods. After discussing these findings, this paper offers recommendations to mitigate disaster loss and damage to SMEs. In particular, it highlights the importance of community-level awareness and mitigation efforts rather than administrative unit-level mitigation plans. Also, the government needs to register informal businesses by providing a more flexible business registration mechanism.

Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data

Accurate and timely monitoring of flooded crop areas is crucial for disaster rescue and loss assessment. However, most flooded crop monitoring methods based on synthetic aperture radar (SAR) imagery were developed for rice, which is probably inappropriate for crops with complex canopy structures that strongly attenuate SAR signals. Additionally, these methods often rely on empirical thresholds and region-specific reference samples, limiting their reliability and applicability on a larger spatial scale. To address these issues, we developed a novel flooded crop mapping approach at a regional scale using Sentinel-1 time-series data and an unsupervised Gaussian Mixture Model (GMM). Our approach leverages a Flood Separability Index (FSI) derived from the fitted probability density function of flooded and non-flooded crop areas in a GMM. This allows us to overcome the limitations of manual input selection in previous studies. The multi-temporal GMM was constructed using the time-series images with optimal polarization to estimate the flooded crop extents on a regional scale. We also investigated the scattering mechanisms of three typical crop disaster structures within an agricultural landscape area. Our results indicate that the proposed multi-temporal GMM is robust in crop planting areas with complex canopy structures. The performance of both single-temporal and multi-temporal GMMs surpasses that of baseline methods such as Otsu and K-means. Compared with VV polarization, VH polarization exhibits greater potential for accurately mapping flooded crops in complex agricultural regions. Our approach does not require labeled samples or many predefined parameters, making it fast and feasible for mapping flooded crops with complex canopy structures in large spatial areas.

Multi-Criteria Seismic Risk Assessment Based on Combined Weight-TOPSIS Model and CF-Logistic Regression Model—A Case Study of Songyuan City, China

Urban seismic hazards are natural disasters caused by earthquakes in urban areas, which may lead to serious casualties, the collapse of buildings, infrastructure damage, and other impacts, require huge social resources for recovery and reconstruction, and even affect the security stability and sustainable development of the area. This paper adopts the research idea of “Risk = Hazard × Exposure × Vulnerability ÷ Emergency response and recovery capability” and constructs an evaluation system containing 24 representative indicators on this basis. The CF-logistic regression model is applied in the study to calculate the seismic hazard, while the combined weight-TOPSIS model is used to assess the vulnerability of urban hazard-bearing body. Lastly, the study conducts multi-criteria seismic risk evaluation using the GIS platform. The results show that the overall seismic risk in Songyuan is moderate, with 18.66% of the medium-risk area, 37.68% of the very low risk area, 33.96% of the low-risk area, 8.47% of the high-risk area, and 1.23% of the very high-risk area. The significance of this study is to provide a scientific basis for formulating corresponding disaster prevention and mitigation measures and emergency plans, improving urban disaster prevention and emergency response capabilities, reducing urban earthquake disaster losses, and helping to achieve safe and stable urban development.

Economic impacts of multiple natural disasters and agricultural adaptation measures on supply chains in China

Natural disasters frequently occur in China, leading to both substantial direct economic losses in sectors and superimposed indirect losses that spread through the interregional supply chain network. The agricultural sector is particularly vulnerable to disaster response, emphasizing the importance of evaluating the indirect effects of different disasters and measuring the effectiveness of agricultural adaptation measures for coping and recovery. Here, we presented a compiled dataset of multiple disasters and employed an agent-based multiregional input-output model to simulate the day-by-day direct and indirect losses caused by floods, storms, droughts, typhoons, and frosts in China's interregional supply chain network. Our findings revealed that the consecutive 200+ disasters in 2016 resulted in direct losses of $71.7 billion and indirect losses of $45.6 billion, equivalent to 0.67% and 0.42% of China's total GDP, respectively. Floods emerged as the most economically devastating disaster, accounting for over 70% of the total supply chain losses. The agricultural sector played a significant role, accounting for 18.0% of the total indirect losses. Remarkably, agricultural adaptation measures demonstrated the potential to reduce disaster losses by up to 20% across diverse scenarios. Our study enhances understanding of the superimposed economic impacts of multiple natural disasters occurring in China in one whole year rather than merely aggregating the individual impacts of each disaster, which provides a new perspective for exploring the indirect economic losses caused by the frequent occurrence of disasters. Our study also provides valuable insights to develop effective disaster adaptation strategies, enhance supply chain resilience, and promote sustainable economic development in vulnerable sectors and regions within the context of complex climate conditions in the future.

Prediksi Jarak Luncur Longsoran berdasarkan Parameter Geometri Lereng dan Tipe Batuan

landslide disaster. Based on this fact, a method is needed to predict the range of landslides to minimize the impact of disaster losses. The empirical statistical method is one of the methods that can be used to predict landslides by taking input data from the history of previous landslide events. This research aims to find the best modeling form for sliding distance prediction and which parameters influence a landslide's sliding distance prediction. This study used multiple linear regression methods. The data used in this study are geometric slope parameters in the form of slope height (H), original slope (θ), landslide area (A), and rock type (RT). The data was taken from the 2015-2021 PVMBG landslide investigation report and used the Google Earth and Global Mapper program. Based on the analysis of the best empirical model that can predict the sliding distance of a landslide log Lmax = 0,387 – 0,097 RT + 0,230 log H + 0,458 log A – 0,220 tan θ with an R2 value of 0,94 and an average estimated error of 31,56%. The parameter that has the most influence on the prediction of sliding distance is the area affected by the landslide (A).

Debris flow susceptibility assessment based on information value and machine learning coupling method: from the perspective of sustainable development.

Accurately assessing the susceptibility of debris flow disasters is of great significance for reducing the cost of disaster prevention and mitigation, as well as disaster losses. Machine learning (ML) models have been widely used in the susceptibility assessment of debris flow disasters. However, these models often have randomness in the selection of non-disaster data, which can lead to redundant information and poor applicability and accuracy of susceptibility evaluation results. To address this issue, this paper focuses on debris flow disasters in Yongji County, Jilin Province, China; optimizes the sampling method of non-disaster datasets in machine learning susceptibility assessment; and proposes a susceptibility prediction model that couples information value (IV) with artificial neural network (ANN) and logistic regression (LR) models. A debris flow disaster susceptibility distribution map with higher accuracy was drawn based on this model. The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC), information gain ratio (IGR), and typical disaster point verification methods. The results show that the rainfall and topography were found to be decisive factors in the occurrence of debris flow disasters, and the IV-ANN model established in this study had the highest accuracy (AUC = 0.968). Compared to traditional machine learning models, the coupling model produced an increase in economic benefit of about 25% while reducing the average disaster prevention and control investment cost by about 8%. Based on model's susceptibility map, this paper proposes practical disaster prevention and control suggestions that promote sustainable development in the region, such as establishing monitoring systems and information platforms to aid disaster management.

Structural performance of long-span truss structures in ultrahigh voltage substations under multi-hazard action of earthquake and icing

The transmission line is a vulnerable lifeline system when subjected to extreme loads, such as earthquakes and heavy ice loads. This study numerically investigates the structural performance of ultrahigh voltage long-span truss structures (LSTSs) under the multi-hazard action of earthquake and icing to mitigate disaster losses. A typical LSTS-3-tower-3-line system was selected as an analytical model. Its finite element model was established using a simplified spring model to simulate an ice-covered transmission conductor; it was excited by seven multi-component far-field ground motions. The natural vibration characteristics, structural responses, earthquake failure modes, and bearing capacities of LSTS affected by icing were comprehensively analyzed. The results showed that the seismic response of LSTS decreased, and its total response (induced by the tension of the conductor, ice load, and earthquake) increased with an increase in ice thickness. In addition, icing severely reduced the bearing capacity of LSTS with average reductions of 11.7% and 39.1% when the ice thicknesses were 15 and 50 mm, respectively. Moreover, the influence mechanism of icing on LSTS seismic responses and the failure mechanism of LSTS under the multi-hazard action of earthquake and icing were revealed. Specifically, the failure modes of LSTS, namely flexural (mode 1), mixed (mode 2), and shear failures (mode 3), were determined by the ice condition. Finally, prediction models for the structural responses and bearing capacity of LSTS were developed to facilitate the design of electrical equipment and LSTS and improve design efficiency.

Mechanical testing of intelligent fastening

Loosening of threaded fasteners can lead to a decrease in preload, cause fatigue failure of bolts and seriously reduce the reliability of mechanical structures. Timely detection of such a weakening is an effective method of early prevention of a man-made disaster or economic losses. A promising direction in the field of monitoring the condition of complex structures during their operation is associated with the creation of intelligent materials or smart systems based on the use of sensor elements. The paper considers the main technical problems of operation of an intelligent fastener – a bolted connection with an integrated fiber Bragg grating (VBR). Calculations and strength tests of intelligent fasteners are given, problems arising during the operation of such systems and ways to solve them are described

Strategies for improving resilience of regional integrated energy systems in the prevention–resistance phase of integration

The construction of integrated energy systems can help improve energy efficiency and promote global energy transition. However, in recent years, the occurrence of extreme natural disasters has brought certain threats to the safe and stable operation of the integrated energy system. Thus, it is necessary to improve the ability of the integrated energy system to resist disasters, reduce disaster losses, and restore energy supply as soon as possible, i.e., improve its resilience. Considering the influence of pre-disaster prevention measures and disaster-time operational measures on system disaster resilience and the correlation between the two, this paper proposes a system hardening strategy based on three-layer robust optimization. The upper layer formulates the optimal hardening strategy of the system before the disaster event occurs, the middle layer identifies the failed elements in the worst disaster situation, while the lower layer realizes the system operational optimization by coordinating the energy storage charging and discharging plan of each subsystem. The strategy can reduce the total supply shortage of the integrated energy system and improve the flexibility of the system in the pre-disaster prevention and disaster resistance integration stages.

72 Jam Pertama Pendekatan Komunikasi Risiko Untuk Kesiapsiagaan Bencana

Disasters are caused by the interaction of biophysical systems, human systems, and the built environment. Disaster management with a lack of understanding and context of risk communication. When a major disaster occurs, recovery requires a massive infusion of external public and private resources, but what happens is that conditions are highly unequal, and likely to produce many of the pre-existing inequities in exposure and vulnerability. This research focuses on the first 72 hours of disaster relief through effective communication in the case of the 13 June 2022 tornado in Yogyakarta. The purpose of this study is to provide education about the first 72 hours that must be carried out in handling disaster cases through an effective and central communication channel. The research method used is qualitative, existential phenomenology and the paradigm built is constructivism. The findings in the field show that the people of Yogyakarta still lack knowledge about how to communicate effectively in dealing with disasters such as a lack of evacuation training, minimal communication tools, minimal evacuation route maps, even in the context of a tornado disaster, no residents know what to do. to do and to whom they should communicate first. The potential impact of ineffective communication; more and more victims, disaster losses increased partly because the way to design and build communication is wrong and not on target. Many people speculate about the causes of disasters, the minimal contribution of society to increased exposure to hazards often results in damage to environmental resources such as the collapse of people's houses and damage to public facilities. Initial disaster failure; identifying communication lines to ensure evacuation of highly vulnerable environments should be known before disaster. Improvement of long-term communication lines needs to be improved by mapping community communication patterns, communication tools and who is the first person to contact.Keywords: Risk Communication, Disaster Communication, Disaster Evacuation, Communication Lines

A networked risk perspective for analysing debris flow losses factors considering mitigation measures

ABSTRACT As debris flow disasters are becoming increasingly severe worldwide, it is necessary to deepen the research on disaster losses, especially the impact and role of disaster mitigation measures. Based on the regional disaster system theory, this paper sets an influencing factor system of debris flow losses. Then, from the perspective of networked risk, the risk propagation network is established through the Delphi method. Finally, the social network analysis method is introduced to discuss the risk propagation relationship between these factors from two aspects of the overall network analysis and node centrality analysis. The results reveal the chain-like influence characteristics of factors and clarify the main points relevant departments should emphasise in disaster risk reduction. Environmental and engineering mitigation measures strongly influence other factors, thus dominating the risk propagation in forming debris flow losses. The peril of debris flow, residents’ escape ability, and emergency rescue can be easily influenced by other factors, so increasing the resistance of these sensitive factors to the source of influence would support loss reduction. The research results can be applied to risk assessment and loss reduction of debris flow disasters.

Risk Assessment of Maize Yield Losses in Gansu Province Based on Spatial Econometric Analysis

The frequent occurrence of meteorological disasters in China has caused huge losses to agriculture. Risk assessment serves as a bridge from disaster crisis management to disaster risk management. Therefore, it is necessary to carry out a refined comprehensive risk assessment of meteorological disasters in typical areas. However, several limitations remain in the disaster loss risk research, such as too coarse resolution and too single risk indicator. Additionally, less research has examined geographical information on risk clustering and barycenter migration, as well as temporal information on the sustainability of trends. Consequently, it is significant to unearth the geographical and temporal information on disaster loss and identify the refined spatial and temporal evolution pattern of crop risk. For this reason, we evaluated the risk of corn production in Gansu Province. First, based on maize yield data, a risk evaluation index system was constructed using the characteristics of variation trends, fluctuations, and extreme values of disaster losses. Then, the spatial distribution patterns and temporal evolution characteristics of maize production risks on a county scale in Gansu Province were determined using spatial analysis and climate diagnosis technology. The results show that there is a large interdecadal fluctuation in risk. In the 1980s, 1990s, 2000s, and 2010s, the average yield reduction rates of maize in Gansu Province were −11.8%, −12.6%, −8.7%, and −8.5%, and the proportions of counties with severe yield reduction were 34.8%, 44.4%, 20.8%, and 9.7%, respectively. Second, most counties belong to medium-low or low-risk areas for maize production. High-risk counties are primarily located in eastern and southern Gansu, whereas low-risk counties are mostly found along the Hexi Corridor. Third, most risk indicators exhibit some geographical aggregation. The Jiuquan region falls within the low-low-risk aggregation zone. In contrast, the Qingyang region is a high-high aggregation zone with a gradual expansion trend. Four, each risk indicator’s geographical barycenter migrates over a complicated path, but the direction and distance vary considerably. The comprehensive risk migrates along the south-northwest-southeast trajectory, albeit at a shorter distance. Five, the proportion of counties with a medium, medium-severe, severe, and total yield reduction tended to decline. In addition, the annual precipitation is significantly or very significantly correlated with most risk indicators and the comprehensive risk level. The results can guide agricultural production processes at all levels, as well as government disaster prevention.

An Earth Observation Framework in Service of the Sendai Framework for Disaster Risk Reduction 2015–2030

The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions.

Guest Editorial: Security of Communication Protocols in Industrial Control Systems (ICSs)

Over the past decade, the cyber security community has witnessed a trend in which advanced adversaries – such as nation-state actors and cybercriminal groups – are increasingly shifting their exploitation efforts from IT systems to OT and Industrial Control Systems (ICSs). This trend stems from the fact that a compromise of an ICS can have far greater consequences than that of a traditional IT system – potentially resulting not only in financial losses but also losses of human life and/or major environmental disasters.

Changes in Tropical Cyclone Disasters Over China During 2001–2020

AbstractTropical cyclones (TCs) constitute one of the major types of meteorological disasters in China, causing extensive damage. Based on the latest quality‐controlled provincial‐level TC disaster losses data over China from 2001 to 2020, the spatiotemporal variation in TC disaster and its link with TC activity in the form of prevailing track, precipitation, and intensity were investigated. Results showed that the areas impacted by TCs expanded significantly in the past decade (2011–2020) compared with the previous decade (2001–2010) throughout China, while the direct economic losses resulting from TCs did not increase significantly. Regionally, TC disaster losses increased in Northeast China but decreased in Fujian province. Causal analysis indicated that the increased influential TC (ITC) frequency and TC precipitation together led to the increase in damage for northern China. Also, more westward ITCs resulted in the increase in TC disasters for southwestern China. For southeastern China, the reduction in TC disasters was related to the northward shift in the northwestward ITC track. Although TC disasters increase with exposure, they are heavily influenced by TC track, precipitation, and intensity. Our findings suggest that more attention should be paid to TC prediction and early warning in northern China, especially Northeast China, where TC disasters are growing but there is greater vulnerability and insufficient capacity for TC prevention and mitigation.

Estimating natural disaster loss using improved daily night-time light data

Satellite-observed night-time light data has been widely applied to estimate natural disaster loss, which is important for evaluating progress of SDG 11 and 13, but large uncertainty of the time series data hindered the evaluation in high accuracy. This study aims to estimate power outage, a proxy of economic loss, after natural disasters using VIIRS daily night-time light data with improved quality, which is able to capture abrupt change of the light. Firstly, an angular normalization algorithm was developed to generate a stable time series of night-time light data with less fluctuation, and it was validated with different metrics. The algorithm was then applied to analyze power outage after 2017 Maria Hurricane in Puerto Rico, and it was found that the Pearson correlation coefficient between the improved night-time light data and power supply is 0.929 in Puerto Rico, suggesting that the data is accurate to reflect the power supply. For the eight regions in Puerto Rico, they totally lost 78.01% of the power supply after the hurricane, while Caguas, Guayama and Humacao are the regions suffering from the hurricane most severely, losing 94.29%, 92.85% and 88.38% of power supply, respectively, and their recovery speeds are also relatively slow compared to other regions. In addition, we discuss the possibility of using the night-time light indicator for estimating GDP loss of service and industry due to disaster-derived power outage.