scholarly journals A Proposal on Establishment of Early Traffic Warning Systems against Heavy Rainfall Based on Social Economic Losses

2008 ◽  
Vol 15 ◽  
pp. 1-11 ◽  
Author(s):  
Hiroyasu OHTSU ◽  
Yuichiro UMEKAWA
Keyword(s):  
Heavy Rainfall ◽  
Social Economic ◽  
Economic Losses ◽  
Warning Systems

scholarly journals The Role of Seismological Networks in Civil Protection in Low and High Seismic Hazards

2021 ◽  
Author(s):  
Erzsébet Győri ◽  
Arman Bulatovich Kussainov ◽  
Gyöngyvér Szanyi ◽  
Zoltán Gráczer ◽  
Kendebay Zhanabilovich Raimbekov ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Civil Protection ◽  
Warning Systems ◽  
Ground Shaking ◽  
Research Areas ◽  
Seismic Networks ◽  
Strong Ground

Earthquakes are one of the most devastating natural disasters on Earth, causing sometimes huge economic losses and many human casualties. Since earthquake prediction is not yet possible, the purpose of civil protection is to reduce damage and protect human lives, in which the seismological networks of different countries play a very important role. Special applications of seismic networks are the early warning systems that can be used to protect vulnerable infrastructures using automated shutdown procedures, to stop high velocity trains and to save lives if the general public is notified about imminent strong ground shaking. In this paper, we describe the aims and operation of seismological networks, covering in more detail the early warning systems. Then we delineate the seismotectonic settings and seismicity in Hungary and Kazakhstan, furthermore, describe the operating seismological networks and the related scientific research areas with emphasis on civil protection. Hungary and Kazakhstan differ not only in the size of their territory, but also in their seismicity, therefore, in addition to the similarities, there are also significant differences between the aims and problems of their seismological networks.

scholarly journals METHODS TO CALCULATE THE SOCIAL-ECONOMIC DAMAGE CAUSED BY FLOODS

2020 ◽  
pp. 5-11
Author(s):  
Givi Gavardashvili ◽  
◽  
Martin Vartanov ◽  
Keyword(s):  
Social Economic ◽  
Dam Failure ◽  
Economic Losses ◽  
Economic Damage ◽  
Earth Dams ◽  
Hydraulic Facility ◽  
Industrial Facilities ◽  
High Rise ◽  
Type Wave ◽  
The Social

The volumes of the water reservoirs flooded with high-rise hydrotechnical facilities, including high-rise earth dams, often range from several hundreds of millions to tens of billions of cubic meters and even more. The present paper describes the methodology to calculate the social-economic losses for the facilities flooded and destroyed by a tsunami-type wave in case of a possible high-rise hydraulic facility accident.The social-economic damage caused by a dam failure can be viewed as a sum of dam-age caused by human victim, destruction of hydraulic and industrial facilities and agricul-ture, pond economy, forestry and communal services.

Assessment of Flood Early Warning Systems with Social Media

2021 ◽  
Author(s):  
Thierry Hohmann ◽  
Judit Lienert ◽  
Jafet Andersson ◽  
Darcy Molnar ◽  
Peter Molnar ◽  
...  
Keyword(s):  
Social Media ◽  
Performance Assessment ◽  
Early Warning ◽  
Early Warning Systems ◽  
Probability Of Detection ◽  
Economic Losses ◽  
Warning Systems ◽  
Flood Events ◽  
Spatial Coverage ◽  
De Bruijn

<p><strong>Introduction</strong></p><p>Flood early warning systems (FEWS) can reduce casualties and economic losses (UNEP, 2012). The EC Horizon 2020 project FANFAR (www.fanfar.eu) aims to co-develop a FEWS in West Africa together with stakeholders, predicting streamflow and return period threshold exceedance (Andersson et al., 2020). A Multi-Criteria Decision Analysis (MCDA) indicated, that stakeholders find information accuracy especially important, among a broad set of fundamental objectives (Lienert et al., 2020). Social media have the potential to support accuracy assessment by detecting flood events (Lorini et al., 2019; de Bruijn et al., 2019) due to their large spatial coverage (Restrepo-Estrada et al., 2018). We investigated the potential of social media to assess FANFAR forecast accuracy.</p><p> </p><p><strong>Research Approach</strong></p><p>FANFAR forecasts are based on HYPE, which is a semi-distributed land-cover and sub-catchment based hydrological model (Arheimer et al., 2020). We lumped the forecasted flood risk (FFR) on a country scale and compared it to flood events detected on Twitter, using an algorithm (FEDA) developed by de Bruijn et al. (2019). FEDA detects flood-related tweet bursts based on regionally and temporally adjusted thresholds (de Bruijn et al., 2019). We compared FEDA detected events with floods from the disaster database EM-DAT (https://www.emdat.be/), to find if tweets indicate flooding. We also compared FEDA to the lumped FFR to identify false positives (FP), false negatives (FN), and true positives (TP), from which we deduced the probability of detection (POD) and false alarm rate (FAR). We further calculated the correlation of single flood-related tweets with the lumped FFR and investigated seasonality, lag, and the influence of rainfall.</p><p> </p><p><strong>Findings</strong></p><p>The detailed findings are described in Hohmann (2021). FEDA (i.e., tweets) and EM-DAT events (i.e., floods) mostly occurred in the same period. However, FEDA detected shorter and more frequent events than EM-DAT. In the Upper Niger, POD<sub>FEDA</sub> and FAR<sub>FEDA</sub> (deduced from FEDA) were of similar order of magnitude as the POD<sub>S</sub> and FAR<sub>S</sub> (deduced from streamflow) but were different in the Lower Niger region. This suggests that tweets can be employed additionally to e.g. streamflow timeseries as a complementary way to evaluate accuracy. Correlation analysis between single flood-related tweets and the lumped FFR showed no relationship. We also did not find a systematic influence of seasonality or a lagged response between tweets and FFR. The correlation coefficients between tweets and rainfall ranged from 0.1-0.9, but were mostly non-significant. This suggests that a performance assessment based on single tweets is not (yet) adequate. Also, since FEDA does not differentiate between pluvial and fluvial floods, it is less suited to assess the accuracy of FANFAR. Our findings suggest the need for inclusion of other factors into the performance assessment of FEWSs, such as regional thresholds to identify TP, FP, and FN. Also, rainfall causing pluvial flooding must be considered. Finally, our approach is limited to Twitter. Further research should assess the potential of e.g. Facebook to be included in FEWS performance assessment. The question whether social media, FEWSs, or EM-DAT are correct remains, and is in our opinion best addressed by employing multiple data sources.</p>

scholarly journals Critical Analysis of the Value of Drought Information and Impacts on Land Management and Public Health

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1064
Author(s):  
Tingting Liu ◽  
Kelly Helm Smith ◽  
Richard Krop ◽  
Tonya Haigh ◽  
Mark Svoboda
Keyword(s):  
Public Health ◽  
Land Management ◽  
Critical Analysis ◽  
Public Information ◽  
Weather Forecast ◽  
Economic Loss ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Warning Systems ◽  
Bureau Of Land Management

This paper reviews previous efforts to assign monetary value to climatic or meteorological information, such as public information on drought, climate, early warning systems, and weather forecast information. Methods and tools that have been explored to examine the benefits of climatic and meteorological information include the avoided cost, contingent valuation, choice experiments, benefit transfer, and descriptive approaches using surveys. The second part of this paper discusses specific considerations related to valuing drought information for public health and the Bureau of Land Management. We found a multitude of connections between drought and the land management and health sectors in the literature. The majority of the papers that we summarized only report biophysical change, because the economic losses of drought are not available. Only a few papers reported economic loss associated with drought. To determine the value of drought information, we need to know more about the role it plays in decision making and what sources of drought information are used in different sectors. This inventory of methods and impacts highlights opportunities for further research in valuing drought information in land management and public health.

scholarly journals Urban resilience to floods in parts of Makassar, Indonesia

2020 ◽  
Vol 200 ◽  
pp. 01007
Author(s):  
Meydelin Isani Thoban ◽  
Dyah Rahmawati Hizbaron
Keyword(s):  
Open Space ◽  
Social Economic ◽  
Urban System ◽  
Early Warning Systems ◽  
Urban Systems ◽  
Warning Systems ◽  
Urban Resilience ◽  
Contributing Factors ◽  
Multi Criteria Evaluation ◽  
The Government

Makassar – the largest and fastest growing area in eastern Indonesia – experienced significant number of damages and losses due to recurrent floods. In early 2019, the flood disaster exposed the urbanized area and inundated 1,658 houses and caused 9,328 impacted population. These figures imply that Makassar needs to create concerted efforts to improve its currently low resilience to floods. This study was designed to assess the urban resilience to floods in Makassar to provide the government with reference for evaluation and identify the most contributing factors to the resilience. In this context, resilience was assessed in four urban systems, namely physical, social, economic, and institutional, in every unit of analysis, i.e., flood-affected districts. The research data included building density, green open space, population density, the number of economically disadvantaged households, community’s subsistence funds, and the availability of early warning systems and disaster emergency stations. The physical, social, economic, institutional, and equal scenarios of resilience were modeled using the Spatial Multi-Criteria Evaluation (SMCE). The results showed that the districts in Makassar were moderately resilient to floods and that the resilience of each urban system shaped the overall resilience. Tamalate and Rappocini sub districts had the lowest resilience values, whereas Manggala was estimated as the most highly resilient district in several scenarios.

Analysis of the Evolution of a Meiyu Frontal Rainstorm Based on Doppler Radar Data Assimilation

2020 ◽  
Author(s):  
Hongli Li ◽  
Yang Hu ◽  
Zhimin Zhou
Keyword(s):  
Heavy Rainfall ◽  
Doppler Radar ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Economic Losses ◽  
Mature Stage ◽  
Convective System ◽  
Low Level ◽  
Sufficient Energy ◽  
Study Results

<p>During the Meiyu period, floods are prone to occur in the middle and lower reaches of the Yangtze River due to the highly concentrated and heavy rainfall, which caused huge life and economic losses. Based on numerical simulation by assimilating Doppler radar, radiosonde, and surface meteorological observations, the evolution mechanism for the initiation, development and decaying of a Meiyu frontal rainstorm that occurred from 4th to 5th July 2014 is analyzed in this study. Results show that the numerical experiment can well reproduce the temporal variability of heavy precipitation and successfully simulate accumulative precipitation and its evolution over the key rainstorm area. The simulated “rainbelt training” is consistent with observed “echo training” on both spatial structure and temporal evolution. The convective cells in the mesoscale convective belt propagated from southwest to northeast across the key rainstorm area, leading to large accumulative precipitation and rainstorm in this area. There existed convective instability in lower levels above the key rainstorm area, while strong ascending motion developed during period of heavy rainfall. Combined with abundant water vapor supply, the above condition was favorable for the formation and development of heavy rainfall. The Low level jet (LLJ) provided sufficient energy for the rainstorm system, and the low-level convergence intensified, which was an important reason for the maintenance of precipitation system and its eventual intensification to rainstorm. At its mature stage, the rainstorm system demonstrated vertically tilted structure with strong ascending motion in the key rainstorm area, which was favorable for the occurrence of heavy rainfall. In the decaying stage, unstable energy decreased, and the rainstorm no longer had sufficient energy to sustain. The rapid weakening of LLJ resulted in smaller energy supply to the convective system, and the stratification tended to be stable in the middle and lower levels. The ascending motion weakened correspondingly, which made it hard for the convective system to maintain.</p>

scholarly journals Climate Risk and Early Warning Systems: Adaptation Strategies for the Most Vulnerable Communities

2021 ◽  
Vol 18 (02) ◽  
Author(s):  
Jessica Bhardwaj ◽  
Atifa Asghari ◽  
Isabella Aitkenhead ◽  
Madeleine Jackson ◽  
Yuriy Kuleshov
Keyword(s):  
Early Warning ◽  
Climate Adaptation ◽  
Early Warning Systems ◽  
Adaptation Strategies ◽  
Climate Risk ◽  
Economic Losses ◽  
Natural Environments ◽  
Warning Systems ◽  
Vulnerable Communities ◽  
Policy And Planning

Climate risk and resultant natural disasters have significant impacts on human and natural environments. It is common for disaster responses to be reactive rather than proactive due to inadequate policy and planning mechanisms—such reactive management responses exacerbate human and economic losses in times of disaster. Proactive disaster responses maximize disaster resilience and preparation efforts in non-disaster periods. This report focuses on proactive, localized, and inclusive adaptation strategies for addressing impacts of three natural hazards: drought, floods, and tropical cyclones. Four key synergistic climate adaptation strategies are discussed—Post Disaster Reviews, Risk Assessments, Early Warning Systems and Forecast-based Financing. These strategies are further supported with a number of case studies and recommendations that will be of assistance for policymakers in developing evidence-based adaptation strategies that support the most vulnerable communities in the transition towards regarding disaster as a risk as opposed to a crisis.

scholarly journals Spatiotemporal Landslide Susceptibility Mapping Incorporating the Effects of Heavy Rainfall: A Case Study of the Heavy Rainfall in August 2021 in Kitakyushu, Fukuoka, Japan

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3312
Author(s):  
Jiaying Li ◽  
Weidong Wang ◽  
Yange Li ◽  
Zheng Han ◽  
Guangqi Chen
Keyword(s):  
Landslide Susceptibility ◽  
Heavy Rainfall ◽  
Characteristic Curve ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Support Vector ◽  
Landslide Risk ◽  
Chi Square ◽  
Area Index ◽  
The Impact

Landslide represents an increasing menace causing huge casualties and economic losses, and rainfall is a predominant factor inducing landslides. Landslide susceptibility assessment (LSA) is a commonly used and effective method to prevent landslide risk, however, the LSA does not analyze the impact of the rainfall on landslides which is significant and non-negligible. Therefore, the spatiotemporal LSA considering the inducing effect of rainfall is proposed to improve accuracy and applicability. In this study, the influencing factors are selected using the chi-square test, out-of-bag error and multicollinearity test. The spatial LSA are thus obtained using the random forest (RF) model, deep belief networks model and support vector machine, and compared using receiver operating characteristic curve and seed cell area index to determine the optimal assessment result. According to the heavy rainfall characteristics in the study area, the rainfall period is divided into four stages, and the effective rainfall model is employed to generate the rainfall impact (RI) maps of the four stages. The spatiotemporal LSAs are obtained by coupling the optimal spatial LSA and various RI maps and verified using the landslide warning map. The results demonstrate that the optimal spatiotemporal LSA is obtained using the spatial LSA of the RF model and temporal LSA of the rainfall data in the peak stage. It can predict the area where rainfall-induced landslides are likely to occur and prevent landslide risk.

scholarly journals Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**

2021 ◽  
Vol 15 (02) ◽  
pp. 11-17
Author(s):  
Olivier Debauche ◽  
Meryem Elmoulat ◽  
Saïd Mahmoudi ◽  
Sidi Ahmed Mahmoudi ◽  
Adriano Guttadauria ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Warning ◽  
Large Scale ◽  
Prediction Models ◽  
Warning System ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Warning Systems ◽  
Large Scale Data ◽  
Set Up

Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.

scholarly journals Impact of Boreal Summer Intra-Seasonal Oscillations on the Heavy Rainfall Events in Taiwan during the 2017 Meiyu Season

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 205 ◽  
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Jen-Her Chen ◽  
Liping Deng
Keyword(s):  
Heavy Rainfall ◽  
Boreal Summer ◽  
Economic Losses ◽  
Global Forecast System ◽  
Forecast System ◽  
Rainfall Events ◽  
The Impact ◽  
Meiyu Season ◽  
Heavy Rainfall Events ◽  
Seasonal Oscillations

During May and June (the Meiyu season) of 2017, Taiwan was affected by three heavy frontal rainfall events, which led to large economic losses. Using satellite observations and reanalysis data, this study investigates the impact of boreal summer intra-seasonal oscillations (BSISOs, including a 30–60 day ISO mode named BSISO1 and a 10–30 day ISO mode named BSISO2) on the heavy rainfall events in Taiwan during the 2017 Meiyu season. Our examinations show that BSISO2 is more important than BSISO1 in determining the formation of heavy rainfall events in Taiwan during the 2017 Meiyu season. The heavy rainfall events generally formed in Taiwan at phases 4–6 of BSISO2, when the enhanced southwesterly wind and moisture flux convergence center propagate northward into the Taiwan area. In addition, we examined the forecast rainfall data (at lead times of one day to 16 days) obtained from the National Centers for Environmental Prediction Global Forecast System (NCEPgfs) and the Taiwan Central Weather Bureau Global Forecast System (CWBgfs). Our results show that the better the model’s capability in forecasting the BSISO2 index is, the better the model’s capability in forecasting the timing of rainfall formation in Taiwan during the 2017 Meiyu season is. These findings highlight the importance of BSISO2 in affecting the rainfall characteristics in East Asia during the Meiyu season.

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