Disaster Early Warning Research Articles

Evaluation of the end-users of disaster risk warnings in Brazil

Disaster risk warnings have been issued by the National Centre for Monitoring and Early Warning of Natural Disasters (Cemaden) to municipalities susceptible to floods and landslides in Brazil since December 2011. A survey of the end-users was developed in order to obtain an evaluation related to quality, anticipation and precision of the warnings. Using an explorative approach, a semi-structured questionnaire was available online to be filled in by the individuals responsible for receiving the warnings. The analysis of the results was supported by statistical techniques, to allow association among the variables. The majority of the users did not report difficulties in understanding the warning texts. However, they reported the inefficiency in receiving warnings by email and suggested that faster tools would be a better solution, i.e. a short message service. The improvements in the alerts could be informed by the results achieved in this survey.

Comprehensive economic loss assessment of disaster based on CGE model and IO model—A case study on Beijing “7.21 Rainstorm”

The economic loss evaluation of disaster is the basis for disaster prevention and disaster emergency management and thus needs to be solved urgently. Computable general equilibrium (CGE) model and input-output (IO) model are the major disaster assessment methods, each of which has its own strengths and weaknesses currently. However, few comparative researches have been conducted on the economic losses of one disaster evaluated by CGE Model and IO Model based on the same disaster impact. Therefore, this paper first constructs multi-sectoral CGE model and IO model, which are commonly used in disaster loss assessment. Then, taking the Beijing “7.21 Rainstorm” in 2012 as an example, the direct losses of agricultural and transportation sectors are introduced to the basic model. After that, the economic loss ratio and comprehensive losses based on the two models can be obtained. Finally, by comparing the assessment results, the main conclusions of the evaluation mechanism are summarized. The results showed that: (1) The CGE model has a greater spillover effect upon sectors and a wider distribution of disaster-affected sectors than the IO model; (2) the range of economic loss determined by the assessment results of the CGE model and the IO model is [67.9396, 74.2739] (100 million yuan); (3) agricultural, transportation, and mining sectors are high-sensitivity sectors to rainstorm disasters. Through the comparison of the two models, the accurate economic loss of disaster can be obtained, according to which governments can take targeted measures for the prevention and early warning of disasters.

A review of technologies and applications for smart construction

The internet of things, artificial intelligence and cloud computing offer new opportunities for smart applications in many industries. Potential benefits include improvements of industrial processes, products and services, greater dependability and lower operational costs. This paper reports on an analysis of trends, opportunities and challenges for smart construction by conducting a review of recently published scientific articles. It provides an overview of emerging smart construction applications in areas such as construction monitoring, construction site management, safety at work, early disaster warning, and resources and assets management.

Disaster Early Warning Systems: The Potential Role and Limitations of Emerging Text and Data Messaging Mitigation Capabilities.

The increased risk of mass accidents or major catastrophes taking place necessitates the organization of remedial measures to help protect against these unusual events and adequate preparation in order to minimize their effects. One such initiative is the early notification of residents within a specific area about the risk of a particular calamity. Nowadays, the prevalence of mobile devices enables the installation of various mobile applications allowing for the communication and receiving of information about potential dangers. In many countries there are variously developed systems of notification in place based specifically on text messages. Currently, new laws introduced in Poland establish that it is the obligation of operators of mobile networks to send text messages to all customers of these networks who are within the area where there is a serious risk of a catastrophe. Such messages are in the form of a short alert, to be sent only in extraordinary situations when there is an immediate threat to health or life. The alert is intended to help in the avoidance of danger or to mitigate its impact. This article presents the potential implementation of the early warning system based on text message alerts in Poland, and in particular focuses on decreasing the risks associated with natural disasters. While early text messaging is essential to disaster communications and mitigation, the article further states that means must be found to ensure equal access to the most vulnerable populations and all those, vulnerable and not, who do not have immediate access to text messaging systems. (Disaster Med Public Health Preparedness. 2019;13:709-712).

The threat of extreme wave disasters and coastal abrasion in the coastal areas of Makassar City

Global warming has a great effect on the climate changes and the hydro-oceanography activities in the ocean, has caused threats of the extreme wave disasters and the coastal abrasion along the coastal areas. This research aims to investigate the level of extreme wave threats and the comparative descriptive analysis method. To look at the causality correlation between the study parameter and the interpretation parameter which is used to conclude the disaster level of threat of the extreme wave and the coastal abrasion. The data analysis uses the software of QGIS 2.8.11, and the analysis table is taken from the regulation of chief of the National Disaster Management Agency number 2 of 2012, which has been modified by the author based on the local conditions of the research location. The research results indicate that the coastal areas of Makassar city have threats of the extreme waves and the coastal abrasion with the total threat value ranging from 1,30-3,00. The threat of extreme wave disaster and the category of the abrasion were high in the coastal area of Tamalate, which has the total threat values of to 3,00, while the threat categories in the sub-districts of Wajo, Ujung Tanah, and Tallo were medium ranging from 1,70 to 2,20. The lowest categories were found in the sub-districts of Biringkanaya, Tamalanrea, and Mariso of which the threats range from 1,3 to 1,6. Decreasing the disaster risk can be done through several strategies, including developing a disaster management plan, establishing a program of decreasing disaster risk, creating an early disaster warning system, and disseminating the program of decreasing disaster risk to the coastal community.

基于物联网的山洪灾害监测预警系统运行维护管理平台的设计及应用

基于物联网的山洪灾害监测预警系统运行维护管理平台的设计及应用

PENERAPAN ALGORITMA CLUSTERING DALAM MENGELOMPOKKAN BANYAKNYA DESA/KELURAHAN MENURUT UPAYA ANTISIPASI/ MITIGASI BENCANA ALAM MENURUT PROVINSI DENGAN K-MEANS

Natural disasters are natural events that have a large impact on the human population. Located on the Pacific Ring of Fire (an area with many tectonic activities), Indonesia must continue to face the risk of volcanic eruptions, earthquakes, floods, tsunamis. Application of Clustering Algorithm in Grouping the Number of Villages / Villages According to Anticipatory / Natural Disaster Mitigation Efforts by Province With K-Means. The source of this research data is collected based on documents that contain the number of villages / kelurahan according to natural disaster mitigation / mitigation efforts produced by the National Statistics Agency. The data used in this study is provincial data consisting of 34 provinces. There are 4 variables used, namely the Natural Disaster Early Warning System, Tsunami Early Warning System, Safety Equipment, Evacuation Line. The data will be processed by clustering in 3 clushter, namely clusther high level of anticipation / mitigation, clusters of moderate anticipation / mitigation levels and low anticipation / mitigation levels. The results obtained from the assessment process are based on the Village / Kelurahan index according to the Natural Disaster Anticipation / Mitigation Efforts with 3 provinces of high anticipation / mitigation levels, namely West Java, Central Java, East Java, 9 provinces of moderate anticipation / mitigation, and 22 other provinces including low anticipation / mitigation. This can be an input to the government, the provinces that are of greater concern to the Village / Village According to the Natural Health Disaster Mitigation / Mitigation Efforts based on the cluster that has been carried out.Keywords: Data Mining, Natural Disaster, Clustering, K-Means

Multistep-ahead forecasting of chlorophyll a using a wavelet nonlinear autoregressive network

Multistep-ahead forecasting is essential to many practical problems, such as the early warning of disasters. However, existing studies mainly focus on current-time or one-step-ahead prediction since forecasting multiple steps continuously presents difficulties, such as accumulated errors and long-term time series modeling. In this paper, an effective multistep-ahead forecasting model wavelet nonlinear autoregressive network (WNARNet), which integrates the wavelet transform and a nonlinear autoregressive neural network (NAR), is proposed for the forecast of chlorophyll a concentration. The wavelet transform decreases the accumulative errors by dividing complicated time series into simpler ones. Simultaneously, the NAR maintains the dependencies between the time series. The buoy monitoring data of the Wenzhou coastal area obtained in 2014-2015 is used to verify the feasibility and effectiveness of WNARNet. The model performs well in predicting the dynamics of chlorophyll a and it is able to predict different horizons flexibly and accurately without training new models. Furthermore, experimental results demonstrate that WNARNet significantly outperforms other benchmark methods of multistep-ahead forecasting. When forecasting 20 steps ahead, the r of WNARNet is 0.08 higher and the RMSE is 0.04 lower than the values of the benchmark models. Therefore, the newly proposed approach represents a promising and effective method for the future prediction of chlorophyll a.

Landslide monitoring analysis of single-frequency BDS/GPS combined positioning with constraints on deformation characteristics

In order to optimise the selection of landslide monitoring points and save the cost of monitoring, a geological constitutive model was constructed by using 3D laser scanning and geological borehole data to simulate the relationship between rainfall and deformation. Thus, the main occurrence area and maximum deformation of the landslide were determined. Aiming at the deficiency of the single-epoch redundancy of the single-frequency GNSS receiver and the poor accuracy, this paper proposes a single-frequency BDS/GPS combined positioning and monitoring scheme with constraint of deformation features to restrict the search range of single-frequency ambiguity, obviously increase the ambiguity fixed success rate and then improve the BDS/GPS positioning accuracy. By contrast experiments, the landslide area obtained by numerical simulation basically matches with the on-site landslide area. The BDS/GPS combined positioning with constraint is consistent with the deep displacement changes, which can well reflect the displacement of the landslide body and make an early warning of disasters.

RESEARCH ON DISASTER EARLY WARNING AND DISASTER RELIEF INTEGRATED SERVICE SYSTEM BASED ON BLOCK DATA THEORY

Abstract. With the continuous development of social economy, the interaction between mankind and nature has become increasingly evident. Disastrous global catastrophes have occurred from time to time, causing huge losses to people's lives and property. All governments recognize the importance of the establishment of disaster early warning and release mechanisms, and it is also an urgent issue to improve the comprehensive service level of emergency response and disaster relief. However, disaster early warning and emergency relief information is usually generated by different departments, and the diverse data sources, difficult integration, and limited release speed have always been difficult issues to be solved. Block data is the aggregation of various distributed (point data) and segmentation (data) big data on a specific platform and make them happen continuous polymerization effect, block data theory is a good solution to cross-sectoral, cross-platform Disaster information data sharing and integration problems. This paper attempts to discuss the integrated service mechanism of disaster information aggregation and disaster relief based on block data theory and introduces a location-based integrated service system for disaster early warning and disaster relief.

Disaster Early Warning and Information Services Meteorology, Climatology and Geophysics Agency’s Employees Performance Observed from their Motivation and Competency

<p><em>Improvement is needed in the early warning system and information services of Indonesia Agency for Meteorology, Climatology and Geophysics (Badan Meteorologi, Klimatologi dan Geofisika-BMKG) in term of speed, and accessibility by rural communities. This paper analyzes the factors related to the motivation and competency of BMKG employees’ performance in giving information services related to early warning system. The aim is to determine the effect of motivation and competency partially and simultaneously toward BMKG’s employees performance. Using quantitative method with proportionate stratified random sampling technique, total 80 respondents were interviewed among 389 population of BMKG’s employees who work on Deputy of Meteorology, Climatology, and Geophysics. The result indicated that the motivation and competency have significant impact to the BMKG’s employees performance. R<sup>2</sup> is 0.832 which means that the motivation and competency simultaneously have significant impact to employees performance about 83.2% while the remaining 16.8% influenced by others variable outside the variables studied. The data analyzed using double linear regression in SPSS version 24, the equation regression obtained is: Y= 9,429 + 0,408X<sub>1</sub> + 0,822X<sub>2</sub>, indicating that effect of motivation factor is lower than competency factor toward employees performance. Therefore BMKG Head Office needs to improve the competency of employees more than motivate them in order to improve its performance on information services and disaster early warning.</em></p>

Correlation between Rainfall and Mass Movements in North Coast Region of Sao Paulo State, Brazil for 2014-2018

The monitoring of related hourly and accumulated rainfall index requires that critical thresholds of accumulated 72 hours rainfall are updated frequently according with the factors and local conditions (natural and anthropic) of each specific risk area. The importance of empirical methods is fundamental to confirm the relationship between rainfall intensity and accumulated rainfall with the mass movement events, in order to establish the critical threshold values. The present work performs an evaluation of the record data of mass movement events occurred in Sao Paulo State North coast region for a 4-year period (2014 to 2018) considering different mass movement characteristics (slope type, magnitude and impact level). Some rainfall values were obtained to show that within these parameters an event related to natural and anthropic features was triggered. A database was created, sorting source of information and municipalities monitored, to implement the correlation between the mass movement events and the rainfall values. To elaborate the event’s map, reliable record data of localization of the mass movement events was selected, as well as the nearest possible raingauges of CEMADEN (National Center for Monitoring and Early Warning of Natural Disasters); also the exact event triggering time, selection by the slope type, the magnitude and the impact level of the mass movement event. The rainfall values of these raingauges allowed the calculation of the accumulated rainfall index for 1, 3, 6, 24, 48, 72 and 96 hours, with the adoption of the 72 hours index for this work. The correlation graphics are divided by the slope type, the magnitude and the impact level of the mass movement event. Different critical thresholds appear, classifying such event by the influence level of triggering factors, natural and/or anthropic.

Linking livestock snow disaster mortality and environmental stressors in the Qinghai-Tibetan Plateau: Quantification based on generalized additive models

Livestock snow disaster occurs widely in Central-to-Eastern Asian temperate and alpine grasslands. The effects of snow disaster on livestock involve a complex interaction between precipitation, vegetation, livestock, and herder communities. Quantifying the relationship among livestock mortality, snow hazard intensity, and seasonal environmental stressors is of great importance for snow disaster early warning, risk assessments, and adaptation strategies. Using a wide-spatial extent, long-time series, and event-based livestock snow disaster dataset, this study quantified those relationships and established a quantitative model of livestock mortality for prediction purpose for the Qinghai-Tibet Plateau region. Estimations using generalized additive models (GAMs) were shown to accurately predict livestock mortality and mortality rate due to snow disaster, with adjusted-R2 up to 0.794 and 0.666, respectively. These results showed that a longer snow disaster duration, lower temperatures during the disaster, and a drier summer with less vegetation all contribute significantly and non-linearly to higher mortality (rate), after controlling for elevation and socioeconomic conditions. These results can be readily applied to risk assessment and risk-based adaptation actions.

Miniaturized Water Flow and Level Monitoring System for Flood Disaster Early Warning

This study presents the performance of a prototype miniaturised water flow and water level monitoring sensor designed towards supporting flood disaster early warning systems. The design involved selection of sensors, coding to control the system mechanism, and automatic data logging and storage. During the design phase, the apparatus was constructed where all the components were assembled using locally sourced items. Subsequently, under controlled laboratory environment, the system was tested by running water through the inlet during which the flow rate and rising water levels are automatically recorded and stored in a database via Microsoft Excel using Coolterm software. The system is simulated such that the water level readings measured in centimeters is output in meters using a multiplicative of 10. A total number of 80 readings were analyzed to evaluate the performance of the system. The result shows that the system is sensitive to water level rise and yielded accurate measurement of water level. But, the flow rate fluctuates due to the manual water supply that produced inconsistent flow. It was also observed that the flow sensor has a duty cycle of 50% of operating time under normal condition which implies that the performance of the flow sensor is optimal.

Applied prospect of modern information technology in relation to mountain flood disaster monitoring and early warning system

The Mountain flood disaster Monitoring and Early Warning (Abbr. MFDMEW) system in China is generating increasing amounts of data as it is developed. Using modern information technology, these data are currently gathered, analyzed, and integrated in an attempt to improve both the level of management and the effciency of the operation. Combined with descriptions of both its data structure and its big-data characteristics (“4V + 1C”), this article analyzes the development trend of MFDMEW system in terms of the information sharing, dynamic early warning model, and standardization; Furthermore, the status of Chinese and international development and practices regarding big-data technology are summarized. Big-data technology, in relation to MFDMEW system, is applied mainly to enhance efficiency and to increase the value of the predictions.

Effects of Loading Rate on Gas Seepage and Temperature in Coal and Its Potential for Coal-Gas Disaster Early-Warning

The seepage velocity and temperature externally manifest the changing structure, gas desorption and energy release that occurs in coal containing gas failure under loading. By using the system of coal containing gas failure under loading, this paper studies the law of seepage velocity and temperature under different loading rates and at 1.0 MPa confining pressure and 0.5 MPa gas pressure, and combined the on-site results of gas pressure and temperature. The results show that the stress directly affects the seepage velocity and temperature of coal containing gas, and the pressure and content of gas have the most sensitivity to mining stress. Although the temperature is not sensitive to mining stress, it has great correlation with mining stress. Seepage velocity has the characteristic of critically slowing down under loading. This is demonstrated by the variance increasing before the main failure of the samples. Therefore, the variance of seepage velocity with time and temperature can provide an early warning for coal containing gas failing and gas disasters in a coal mine.

Diversity of Rainfall Thresholds for early warning of hydro-geological disasters

Abstract. For early warning of disasters induced by precipitation (such as floods and landslides), different kinds of rainfall thresholds are adopted, which vary from each other, on the basis on adopted hypotheses. In some cases, they represent the occurrence probability of an event (landslide or flood), in other cases the exceedance probability of a critical value for an assigned indicator I (a function of rainfall heights), and in further cases they only indicate the exceeding of a prefixed percentage a critical value for I, indicated as Icr. For each scheme, it is usual to define three different criticality levels (ordinary, moderate and severe), which are associated to warning levels, according to emergency plans. This work briefly discusses different schemes of rainfall thresholds, focusing attention on landslide prediction, with some applications to a real case study in Calabria region (southern Italy).

Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China

It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 °C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot).

New integrated monitoring and control system for disaster gas application in coal mine

This paper introduces the composition, working principle and characteristics of the new gas monitoring system, which are used to monitor the gas in the gob area and working face of the mine in real time. According to the oxygen concentration, the spontaneous combustion of gob area “three zones” was determined and divided. In order to carry out disaster early warning, the methane and carbon monoxide super gas concentration changes were studied and analyzed. And the variation law of gas concentration in gob area and coal face in coal mining was obtained. This study provides reference for the follow-up fire prevention and production.

Disaster Early Warning Systems in Nepal: Institutional and Operational Frameworks

Early warning systems (EWS) are recognized in both the Hyogo Framework for Disaster Reduction (2005-2015) and the Sendai Framework for Disaster Risk Reduction (2015-2030) as an important element of disaster risk reduction and hence to the achievement of sustainable development and sustainable livelihoods. An effective EWS enables the concerned authorities and at-risk communities to know about the hazards of the locality, community vulnerability and impending risk, warning messages, and building and mobilizing their response capability to reduce risks. The Government of Nepal has reported significant achievements in the development and implementation of EWS for floods, landslides, and Glacial Lake Outburst Floods (GLOF). However, this study shows that Early Warning systems have covered only a few hazards and location so far, and has not been able to cover all the four components of the effective community based early warning system. The absence of policy and legal frameworks has weakened the efforts of the establishment and strengthening of effective and functional early warning systems in the country. The response capacity building of communities is scattered and project-based. This paper outlines the status of EWS in Nepal, key gaps and challenges, and proposes a framework for a peoplecentred early warning system in the country.