Disaster Assessment Model Research Articles

Statistical Framework to enhance Disaster Resilience: An Empirical Cyclone Disaster Assessment Model

Statistical Framework to enhance Disaster Resilience: An Empirical Cyclone Disaster Assessment Model

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region.

A Cost-Effective Earthquake Disaster Assessment Model for Power Systems Based on Nighttime Light Information

The power system is one of the most important urban lifeline engineering systems. Identifying the damage to the power system is an important task in earthquake disaster assessments. Considering the importance of timeliness and accessibility, a hyperparameter optimization model is proposed to address the assessment of disaster losses in power systems on earthquakes. The power system vulnerability on earthquakes, PSVE, is assessed by the hyperparameter optimization model based on nighttime light information. Through the utilization of the computational resources provided by Google Earth Engine, the accuracy of the baseline model has been significantly improved to 87.9%; meanwhile, the cost-effectiveness in the evaluation process is maintained. The PSVE-based damage evaluation has the potential to aid in assessing earthquake damage to cities’ energy supply, power infrastructure, and lighting. Furthermore, the PSVE-based damage evaluation can provide valuable guidance for prioritizing and efficiently allocating resources for rapid repair and reconstruction efforts.

Fractional Linear Regression Equation in Agricultural Disaster Assessment Model Based on Geographic Information System Analysis Technology

Abstract This article combines geographic information system (GIS) technology and database technology to analyse agricultural, natural disasters. The article uses a fractional linear regression equation to define the comprehensive intensity grading standard of the disaster-causing factors of torrential rain. At the same time, we use GIS to superimpose the agricultural vulnerability index into the storm disaster risk zoning to obtain the degree of agricultural impact under different levels of risk. At the end of the thesis, the model is applied to actual case analysis to verify the effectiveness of the algorithm model.

Application of a Three-Dimensional Deterministic Model to Assess Potential Landslides, a Case Study: Antong Hot Spring Area in Hualien, Taiwan

This study proposes a landslide disaster assessment model combining a fully three-dimensional, physically-based landslide model with high precision of in situ survey data such as surface slip signs, geologic drilling results, underground water observation, and displacement monitoring results over time to perform distribution of potential landslide zones and the size of landslides (area and volume) in the Antong hot spring area in Hualien, Taiwan. The distribution of potential landslide zones in the study area was represented by slope stability safety factors. The results of the analysis showed that the toe of the slope and two upward slopes in the study area were potential landslide areas with safety factors of 1.37, 0.92, and 1.19, respectively. The 3D model analysis results indicated that a landslide could occur at a depth of 20 m at the toe of the slope. Monitoring results for 2015 and 2016 showed that the sliding depth at the toe of the slope was approximately 22.5 m; consequently, the error of landslide depth was only 2.5 m. The simulated results and in situ monitoring results were in good agreement. In addition, the simulated landslide volume was also compared with the results of an empirical equation commonly used in Taiwan to determine their differences. The landslide volumes estimated using the empirical equation were only approximately 38.5% in zone 1, 42.9% in zone 2, and 21.7% in zone 3 of that generated by the proposed model. The empirical equation was used to calculate the landslide volume according to the landslide area, which was subsequently converted into landslide depth. However, the obtained landslide depth was considerably lower than that derived from the in situ monitoring, implying that an empirical estimation approach may result in serious underestimation. Thus, the proposed model could predict landslide area and volume in advance to assist authorities in minimizing loss of life and property damage during a heavy rainfall event.

Spatial case revision in case-based reasoning for risk assessment of geological disasters

Case revision has always been challenging in case-based reasoning (CBR) processes. Most CBR methods used for analyzing geological disasters fail to consider the spatial relationships among geological environmental factors. Therefore, conventional case revision rules do not allow for effective case-based reasoning for geologic disaster assessment. In this study, we first establish a spatial case library of historical geological disasters. Subsequently, these spatial cases are organized, reduced, and weighted using spatial clustering, genetic algorithm and rough set hybrid algorithms, respectively. Based on these efforts, we propose a retrieval method for the spatial cases and evaluate the proposed solution for a target case. Finally, a geological disaster assessment model possessing a spatial case revision function is obtained by combining geographic information system (GIS) technology with the genetic algorithm. Experiments and applications show that our spatial revision approach is effective and that it exhibits a higher classification performance compared to traditional data mining methods with regard to geological disaster assessment. The approach also exhibits a higher accuracy and efficiency than typical spatial CBR or case revision models do. The results of this study thus facilitate rapid geological disaster assessment, making it more facile and convenient for decision makers to execute decisions efficiently and quickly.

Experimental dynamic sediment behavior under storm waves with a 50 year recurrence interval in the Yellow River Delta

The dynamic response of marine sediment from the Yellow River under extreme sea conditions is attracting increasing academic and engineering attention because of the high occurrence frequency of geologic hazards. To simulate the dynamic response process of sediment samples under waves with a 50 year recurrence interval, we collected undisturbed sediment samples from six sites on the intertidal flats of the Yellow River Delta and performed dynamic triaxial experiments to analyze the pore-water pressure and liquefaction process. The empirical patterns of pore-water pressure generation and ranges of sediment parameters were determined, and the factors affecting sediment liquefaction were discussed. Under the cyclic loading of waves with a 50 year recurrence interval, the pore pressure response of sediments at a depth of 4 m could be generalized into three stages: rapid growth, slow growth, and stable maintenance. Moreover, the build-up of pore-water pressure was effectively represented by a logarithmic growth model. The liquefaction characteristics of sediment in the Yellow River Delta were more related to its plasticity index, mean particle size, and clay, silt, and sand contents, as well as the sedimentary history. These factors should be considered in the development of disaster assessment models in coastal environments of the Yellow River Delta.

A Spatial Disaster Assessment Model of Social Resilience Based on Geographically Weighted Regression

Since avoiding the occurrence of natural disasters is difficult, building ‘resilient cities’ is gaining more attention as a common objective within urban communities. By enhancing community resilience, it is possible to minimize the direct and indirect losses from disasters. However, current studies have focused more on physical aspects, despite the fact that social aspects may have a closer relation to the inhabitants. The objective of this paper is to develop an assessment model for social resilience by measuring the heterogeneity of local indicators that are related to disaster risk. Firstly, variables were selected by investigating previous assessment models with statistical verification. Secondly, spatial heterogeneity was analyzed using the Geographically Weighted Regression (GWR) method. A case study was then undertaken on a flood-prone area in the metropolitan city, Seoul, South Korea. Based on the findings, the paper proposes a new spatial disaster assessment model that can be used for disaster management at the local levels.

Typhoon damage assessment model and analysis in Taiwan

Typhoons are one of the world’s most frequently occurring natural disasters that cause severe damage. The losses caused by typhoons are not only related to the strength and structure of a particular typhoon, but also to the population density, home range, and type of economy in the affected area. Taiwan is centrally situated in the main path arc of typhoons generated in the Northwest Pacific Ocean. On average, 3–4 typhoons approach or make landfall in Taiwan yearly. The associated strong winds, violent rains, landslides, and storm surges or upwelling in coastal areas often catastrophically impact the citizens of Taiwan and cause severe damage to the national economy. Losses may amount to billions, or even tens of billions of New Taiwan Dollars. Due to the limitations imposed by the lack of complete data in earlier years, this study focuses on those typhoons with complete records which landed and affected Taiwan from 1965 to 2004. This study considers meteorological indicators including maximum daily rainfall, maximum central wind speeds, lowest central pressure, radius of class seven winds, and period during CWB issued land and sea warning, as well as disaster indicators that reflect damages resulting from typhoons such as the numbers of dead or missing persons, totally destroyed houses, and actual amounts of crop losses. This study aims to statistically analyze the meteorological and disaster indicators, and build a disaster assessment model which allows for a quantified assessment to be conducted. This would enable the relevant government departments to immediately assess or evaluate the disaster upon the receipt of predicted or actual data from a particular typhoon. Additionally, this study evaluated precautionary building principles in terms of the positive effect they have on improving disaster management.

Earthquake Disaster Assessment System

The development of Web Service technology provided an innovational way for data sharing and system integration. The Service-oriented architecture (SOA) dedicated to solve the data operating and sharing between heterogeneous systems. In this paper, an earthquake disaster assessment system was built on the SOA point of view. It integrated the GIS operation and the Web Services for data accessing. With the integration of the services from outside of the system, several of data was available for mapping. The system was used in earthquake emergency response. With the usage of the data from the different system, the earthquake disaster assessment model worked more effective.

Rapid estimation of earthquake loss based on instrumental seismic intensity: design and realization

As a result of our ability to acquire large volumes of real-time earthquake observation data, coupled with increased computer performance, near real-time seismic instrument intensity can be obtained by using ground motion data observed by instruments and by using the appropriate spatial interpolation methods. By combining vulnerability study results from earthquake disaster research with earthquake disaster assessment models, we can estimate the losses caused by devastating earthquakes, in an attempt to provide more reliable information for earthquake emergency response and decision support. This paper analyzes the latest progress on the methods of rapid earthquake loss estimation at home and abroad. A new method involving seismic instrument intensity rapid reporting to estimate earthquake loss is proposed and the relevant software is developed. Finally, a case study using the ML4.9 earthquake that occurred in Shun-chang county, Fujian Province on March 13, 2007 is given as an example of the proposed method.

An integrated expert system for fast disaster assessment

Most of the existing disaster assessment models are based on single method, such as expert system, or one of the multi-criteria decision making (MCDM) methods. This paper proposes an efficient disaster assessment expert system, which integrates fuzzy logic, survey questionnaire, Delphi method and MCDM methods. Two simulation experiments on typhoon and earthquake are introduced to validate the integrated expert system. The satisfaction degrees of the proposed model in both cases are 75% and 74.5%, respectively, which are close to the ideal rate (78%) of the proposed model. The experimental results show that the proposed expert system is not only efficient, fast and accurate, but also robust through self-adaptive study and has strong adaptability to different environments.

Regional Earthquake Disaster Macro Risk Assessment Model Research

Regional earthquake disaster macro risk assessment model can be devided into two parts:economic losses and casualities. through the compare of domestic earthquake disaster as-sessment model,the existing model evaluation index is sole,and it can not reflect the differences of urban earthquake disaster in the area,according to this we built the regional seismic disaster evaluation index system, and use the PCA method to analyze the index,and get the weights of each index,further to build the regional seismic disaster economic loss evaluation index system effectde by multiple in-dex.Take the previous data between 1996 to 2009 as an example to analyze,compare the reckoning and the existing model evaluate results,and check the effectiveness and feasibility of the model.

Coal-rock dynamic disaster assessment model based on electromagnetic emission and least-squares support vector machine

A new non-linear assessment model for coal-rock dynamic disasters is reported in this paper. Coal-rock dynamic disaster assessment parameters are difficult to obtain because assessment samples are usually rare. The least-squares, a non-linear and few sample assessment methods can satisfy requirements on the availability of few samples. Based on the electromagnetic emission (EME) testing results and multiple classification theory of support vector machine, an EME least-squares support vector machine assessment model is built. In a case study, the results show that the EME least-squares support vector machine assessment model for coal and gas outburst is reliable and accurate, and can be applied to the assessment of coal-rock dynamic disasters in coal mines.

An earthquake disaster management mechanism based on risk assessment information for the tourism industry-a case study from the island of Taiwan

Although progress has been made in assessing risks in Taiwan, there remains a need to undertake risk assessment for the Taiwanese tourism industry. Although the conventional rules of thumb usually adopted by the insurance industry for damage assessment may be adequate in normal circumstances, this is not feasible for rare incidents for which there is very little reference material. One such example is earthquake damage. The average period of occurrence of major earthquake in Taiwan is about a hundred years, which makes it impossible to precisely assess average losses. In addition, earthquakes are not like some disasters (such as typhoons or epidemics) for which one can receive early warning. Thus, in this study, we seek to provide a practical mechanism for earthquake disaster risk assessment and management for the tourism industry, focusing on insurance and prevention. The primary objective is to integrate the relevant earth sciences, engineering and insurance data with the concepts of disaster risk management, taking into consideration the characteristics and sources of earthquake-related risks. In short, we develop an earthquake disaster assessment model to apply to risk management in the tourism industry. The practicality of the model is demonstrated with some examples related to the hotel industry in Hualien, Taiwan, an area plagued by frequent earthquakes. The results clearly show the efficacy of the proposed disaster assessment models for the tourism industry.