Development Of Early Warning System Research Articles

Enhancing Flood Risk Management: A Comprehensive Review on Flood Early Warning Systems with Emphasis on Numerical Modeling

During recent decades there has been an increase in extreme flood events and their intensity in most regions, mainly driven by climate change. Furthermore, these critical events are expected to intensify in the future. Therefore, the improvement of preparedness, mitigation, and adaptation counterparts is mandatory. Many scientific fields are involved in this task, but from a meteorological and hydrological perspective, one of the main tools that can contribute to mitigating the impact of floods is the development of Early Warning Systems. In this sense, this paper presents a scientific literature review of some of the most representative Flood Early Warning Systems worldwide, many of which are currently fully operational, with a special focus on the numerical modeling component when it is developed and integrated into the system. Thus, from basic to technically complex, and from basin or regional to continental or global scales of application, these systems have been reviewed. In this sense, a brief description of their main features, operational procedures, and implemented numerical models is also depicted. Additionally, a series of indications regarding the key aspects of the newly developed FEWSs, based on recent trends and advancements in FEWSs development found in the literature, are also summarized. Thus, this work aims to provide a literature review useful to scientists and engineers involved in flood analysis to improve and develop supporting tools to assist in the implementation of mitigation measures to reduce flood damage for people, goods, and ecosystems and to improve the community resilience.

Early Warning System and Crisis Management

This research was made to find out the development map on the research topic with the theme Early Warning Economic Crisis. This study discusses bibliometric mapping of the development of trends in keywords, authors, and journals on digital banking from a number of Scopus-indexed reputable papers published between 1990 and 2021. Early warning systems help organizations reduce losses and are critical in detecting emergencies from occurring. This research contributes to the field of early warning system development and applications in the economic and financial sectors by providing new knowledge and critical insights into a previously unexplored topic. Our research also contributes to a better understanding of the use of Early warning systems in business, finance, and economics. Consequently, accumulating knowledge to detect risks before it is too late is crucial for companies to maintain a competitive advantage in the market.

Estimation of Intensity‐Duration‐Area‐Frequency Relationships Based on the Full Range of Non‐Zero Precipitation From Radar‐Reanalysis Data

AbstractIntensity‐Duration‐Area‐Frequency (IDAF) models provide the mathematical link between precipitation intensities (I), durations (D), areas (A), and frequency of occurrence (F). They play a critical role in hydrological design, areal rainfall hazard quantification, storm characterization, and early warning system development. IDAF models extend the conventional Intensity‐Duration‐Frequency models by accounting for the spatial extent of precipitation (i.e., the area). In this study, we develop IDAF models using the entire non‐zero precipitation intensities, not only the extremes. We use the extended generalized Pareto distribution (EGPD) to model the precipitation intensities. To build the IDAF models, we adopt a data‐driven approach that allows the linkage of EGPD parameters with duration and area, based on empirically determined parametric relationships. The inference of model parameters is done using a global maximum likelihood estimation, and uncertainties are assessed by the bootstrap method. The study area is Switzerland, a topographically complex region of 42,000 km2 with regional precipitation variability and clear seasonality. The study utilizes 17 years of data from CombiPrecip, a radar‐reanalysis product developed by geostatistically merging radar and rain gauge data in an operational setting. We build the IDAF models for the spatiotemporal range of 1–72 hr and 1 to 1,089 km2 at each pixel in the study area. To the best of our knowledge, our study is the first attempt to use the EGPD in IDAF curve modeling. It discusses the use and limitations of CombiPrecip in extreme value analysis and highlights the challenges of modeling areal precipitation in a complex topographical environment.

Development of Early Warning Systems in Croatian Companies

Abstract The challenge to predict the direction of market requirements is an increasingly pronounced challenge of today’s management structures, i.e., how to make timely decisions that ensure the sustainability of the business. In predicting when and where the potential for success will arise or what the sources of threats will be, it is essential to properly implement and continuously use the early warning system by monitoring indicators from the environment. This paper presents a theoretical and practical contribution to understanding the importance of early warning systems in Croatian companies operating in international markets. The introduction includes a discussion on current business conditions, and then the term “early warning system” is conceptually discussed. The research part of the paper presents the results of empirical research on the perception of respondents on the level of development of early warning systems in their companies operating in the international market according to the legal form, size, headquarters, and ownership of the company. The established attitude of the respondents was that the level of development of the early warning system is low, i.e., that they are used primarily by large foreign-owned companies based in more economically developed counties.

Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations

This study marks a significant step toward the future development of river discharges forecasted in real time for flash flood early warning system (EWS) disaster prevention frameworks in the Chugoku region of Japan, and presumably worldwide. To reduce the disaster impacts with EWSs, accurate integrated hydrometeorological real-time models for predicting extreme river water levels and discharges are needed, but they are not satisfactorily accurate due to large uncertainties. This study evaluates two calibration methods with 7 and 5 parameters using the hydrological Cell Distributed Runoff Model version 3.1.1 (CDRM), calibrated by the University of Arizona’s Shuffled Complex Evolution optimization method (SCE-UA). We hypothesize that the proposed ensemble hydrological parameter calibration approach can forecast similar future events in real time. This approach was applied to seven major rivers in the region to obtain hindcasts of the river discharges during the Heavy Rainfall Event of July 2018 (HRE18). This study introduces a new historical extreme rainfall event classification selection methodology that enables ensemble-averaged validation results of all river discharges. The reproducibility metrics obtained for all rivers cumulatively are extremely high, with Nash–Sutcliffe efficiency values of 0.98. This shows that the proposed approach enables accurate predictions of the river discharges for the HRE18 and, similarly, real-time forecasts for future extreme rainfall-induced events in the Japanese region. Although our methodology can be directly reapplied only in regions where observed rainfall data are readily available, we suggest that our approach can analogously be applied worldwide, which indicates a broad scientific contribution and multidisciplinary applications.

Development of early warning system for tsunamis accompanied by collapse of Anak Krakatau volcano, Indonesia

Present tsunami warning systems have been specialized for earthquake-generated tsunamis, but rapidly evaluating the tsunamis caused by volcanic eruptions and/or volcanic sector collapses remains a challenge. In this study, we applied a numerical model to the 2018 Anak Krakatau tsunami event, which was generated by the sector collapse, investigated a tsunami prediction skill by the model, and developed a real-time forecasting method based on a pre-computed database for future tsunamis accompanied by such eruption of Anak Krakatau. The database stores spatiotemporal changes in water surface level and flux, which are simulated under various collapse scenarios, for confined areas in the vicinity of potential source. The areas also cover the locations of observation stations that are virtually placed on uninhabited island surrounding the source area. During an actual volcanic tsunami event, a tsunami is expected to be observed at the observation stations. For real-time tsunami forecasting, the most suitable scenarios to reproduce the observed waveforms are searched quickly in the database. The precomputed results under the identified scenarios are further provided as input for rapid tsunami propagation simulation. Therefore, an effective real-time forecasting can be conducted to densely populated coastal areas located at a considerable distance from the source, such as the coasts of Java and Sumatra. The forecasting performance was examined by applying the method for three hypothetical collapse scenarios assuming different sliding directions. We demonstrated that the tsunamis along the coasts were successfully forecasted. Moreover, we showed that the combination of a pre-computed database and the existence of observation stations near the source area was able to produce appropriate tsunami forecasting for the coastal area even in a volcanic event.

Instrumental response of borehole tensor strainmeters for oblique-incident seismic waves

It has been proven qualitatively that borehole tensor strainmeters (BTSM) could measure three surface-wave-induced strain components in the horizontal-normal plane. But strain waves are usually incident obliquely to borehole axis to induce not only horizontalnormal strain components but also the three other strain components in the plane of vertical normal, which are usually assumed to zero in conventional model of BTSM installed near the earth surface. Therefore quantitative kinematical-parameter measurement of strain waves of wave normal oblique to borehole axis by BTSM is still a challenging problem. In this paper, a scattering model of oblique-incidence seismic waves by an empty borehole is introduced to investigate instrumental responses of BTSM. Besides for the four gauges in conventional BTSM, three other gauges considering the influences of strain components in the plane of vertical normal are also introduced to build dynamic calibration matrix between instrumental and incident strains. It shows that within the effective bandwidth of BTSM, zero-frequency-gain for dynamic calibration matrix could be adopted approximately to build quantitative relation between instrumental strain waves of BTSM and oblique-incidence seismic strain waves. It is shown that the recommended gauge combinations could promise quantitative measurement of kinematic parameters of the incident strain P or S wave if high-resolution insitu calibration of BTSM has been conducted, which is important to the development of early warning system by borehole strainmeters.

Climate change engenders a better Early Warning System development across Sub-Saharan Africa: The malaria case

It is expected that diseases are likely to spread to newer areas, and high-income countries may experience some illnesses that may have been restricted to low or middle-income countries. In addition, following the Intergovernmental Panel on Climate Change, the present study noted that climate change is likely to have many effects on the spatial and temporal distribution of malaria in many Sub-Saharan African countries. This study examines climate change effects on the geographical distribution of malaria occurrence and how extreme climatic events may perhaps be determining factors in the range of vectors for human diseases in SSA in the nearest future. Here, the study appraisals the symbiotic connection of (1) malaria transmission and association with the changes in temperature, rainfall, and humidity as well as their extremes in SSA and (2) the relationship between climate and malaria with the role of climate change in determining upsurge in malaria and meningitis occurrences in the SSA. The study concludes that major drivers of malaria occurrence are climatic elements such as precipitation and temperature. Therefore, we call for a better early Warning System on a proposed roadmap solution for Sub-Saharan Africa.

THE RESPONSE OF ECONOMIC GROWTH TO LIQUIDITY (AN EMPIRICAL STUDY ON PAPUA AND WEST PAPUA, INDONESIA)

This study aims to determine the response of regional economic growth to innovations on liquidity, saving-investment gap, trade openness, inflation and national growth rate. The basic approach of this study is constructed from the principles of open macroeconomy and financial intermediary system. This study used a quarterly data for the period of 2008 – 2019 and employed VAR/VECM/IRF/FEVD. The research novelty will be useful for the development of regional economics and for policy formulation in advancing the economy in the Papua region. Overall, this study found that the response of regional economic growth to innovation on liquidity, saving-investment gap, trade openness, inflation and national growth rate indicates a better synergy for Papua compared to West Papua. The result/novelty of this study is important as an input for the development of early warning system (EWS) and for reformulating the economic development policy and strategy, especially considering that the two provinces still have many underdeveloped districts.

Flood Early Warning and Prediction System for Tributary Streams

Flood Early Warning system development is relatively new and costly area for developing countries, though it has captured attention of the respective parties since such Early Warning System can avoid loss of lives and reduce property damages from floods. Rather than standalone early warning system, warning system with the ability to forecast or predict flood events is more useful for the relevant stake holders where it can be used to plan and act fast. Geological areas around tributary streams are more likely to flood without considerable warnings from the nature as it highly depends on the main river behavior. This system uses Internet of Things (IoT) devices for data capture and transfer. River Water level, Rain status and the Water flow rate (discharge rate) is measured using Sensors. An Artificial Neural Network (ANN) is trained with collected data and integrated with live data feed in order to predict the water level. By doing so forecasting flood events according to the current readings. Notifications are sent via pre-defined notification channels. Due to the higher number of data types considered, ANN predicts water level with considerable accuracy. This collective approach of using ANN and IoT devices has made the forecasting easier and more reliable while using more variables made the predictions more accurate. In addition, these collected data can be used in the future for disaster recovery and mitigation planning since they are kept in cloud environment where public can access.

Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning

AbstractIncreasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning while also cautioning that distinct objectives require distinct data and methods. In particular, we highlight the differences between poverty and malnutrition targeting and mapping, the differences between structural and stochastic deprivation, and the modeling and data challenges of early warning system development. Overall, we urge careful consideration of the purpose and use cases of machine learning informed models.

Exploring rural hospital admissions for diarrhoeal disease, malaria, pneumonia, and asthma in relation to temperature, rainfall and air pollution using wavelet transform analysis

BackgroundClimate variables impact human health and in an era of climate change, there is a pressing need to understand these relationships to best inform how such impacts are likely to change. ObjectivesThis study sought to investigate time series of daily admissions from two public hospitals in Limpopo province in South Africa with climate variability and air quality. MethodsWe used wavelet transform cross-correlation analysis to monitor coincidences in changes of meteorological (temperature and rainfall) and air quality (concentrations of PM2.5 and NO2) variables with admissions to hospitals for gastrointestinal illnesses including diarrhoea, pneumonia-related diagnosis, malaria and asthma cases. We were interested to disentangle meteorological or environmental variables that might be associated with underlying temporal variations of disease prevalence measured through visits to hospitals. ResultsWe found preconditioning of prevalence of pneumonia by changes in air quality and showed that malaria in South Africa is a multivariate event, initiated by co-occurrence of heat and rainfall. We provided new statistical estimates of time delays between the change of weather or air pollution and increase of hospital admissions for pneumonia and malaria that are addition to already known seasonal variations. We found that increase of prevalence of pneumonia follows changes in air quality after a time period of 10 to 15 days, while the increase of incidence of malaria follows the co-occurrence of high temperature and rainfall after a 30-day interval. DiscussionOur findings have relevance for early warning system development and climate change adaptation planning to protect human health and well-being.

Development of Early Warning Systems to monitor e-learning progress

Almost in every course, there are some successful learners and unsuccessful learners. There are many methods to reduce the number of unsuccessful learners. One of the methods is monitoring learning progress which can be used to recognize whether a learner will pass or not. In order to prevent unsuccessful learner, a modelling is needed on learning progress prediction. The modelling on learning progress prediction can be used as an early warning system (EWS) that can warn the learners throughout the learning process. Thus, it will decrease the number of unsuccessful learners and furthermore improve number of successful learners. This study aims to design the Early Warning System (EWS) in e-Learning. Decision making in EWS is a step-based function which is a prediction function of learning progress where the function consists of risk unsuccessful learner variables such as: the number of course accesses, files, URLs, attempted quizzes, and formative test scores. EWS will detect learner progress according to the progress and map the decision threshold.

Research on the architecture of food safety risk intelligence early warning system

Food safety supervision involves all aspects of production, processing and sales. True, reliable and complete intelligence can realize the traceability of the entire process of food safety production, thereby ensuring that food safety incidents are controllable from the source. However, most studies only analyze the food safety risk identification and early warning from the perspective of information flow from the theoretical level, and lack specific applications at the practical level. Therefore, this study analyzes the system requirements and the overall business process of the system, expounds the goals and principles of system design, designs the overall framework of the system, and finally elaborates on the realization of its functions of the different functional modules of the system, so as to provide the early warning system development provides decision support and reference. Finally elaborates the realization of its functions according to the different functional modules of the system, so as to provide decision support and reference for the development of early warning system.

Early warning systems development for agricultural drought assessment in Nigeria.

The existing drought monitoring mechanisms in the sub-Saharan Africa region mostly depend on the conventional methods of drought monitoring. These methods have limitations based on timeliness, objectivity, reliability, and adequacy. This study aims to identify the spread and frequency of drought in Nigeria using Remote Sensing/Geographic Information Systems techniques to determine the areas that are at risk of drought events within the country. The study further develops a web-GIS application platform that provides drought early warning signals. Monthly NOAA-AVHRR Pathfinder NDVI images of 1 km by 1 km spatial resolution and MODIS with a spatial resolution of 500 m by 500 m were used in this study together with rainfall data from 25 synoptic stations covering 32 years. The spatio-temporal variation of drought showed that drought occurred at different times of the year in all parts of the country with the highest drought risk in the north-eastern parts. The map view showed that the high drought risk covered 5.98% (55,312 km2) of the country's landmass, while low drought risk covered 42.4% (391,881 km2) and very low drought risk areas 51.5% (476,578 km2). Results revealed that a strong relationship exists between annual rainfall and season-integrated NDVI (r2 = 0.6). Based on the spatio-temporal distribution and frequency of droughts in Nigeria, drought monitoring using remote sensing techniques of VCI and NDVI could play an invaluable role in food security and drought preparedness. The map view from the web-based drought monitoring system, developed in this study, is accessible through localhost.

Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country

Starting from historic reflections, the current SARS-CoV-2 induced COVID-19 pandemic is examined from various perspectives, in terms of what it implies for the implementation of non-pharmaceutical interventions, the modeling and monitoring of the epidemic, the development of early-warning systems, the study of mortality, prevalence estimation, diagnostic and serological testing, vaccine development, and ultimately clinical trials. Emphasis is placed on how the pandemic had led to unprecedented speed in methodological and clinical development, the pitfalls thereof, but also the opportunities that it engenders for national and international collaboration, and how it has simplified and sped up procedures. We also study the impact of the pandemic on clinical trials in other indications. We note that it has placed biostatistics, epidemiology, virology, infectiology, and vaccinology, and related fields in the spotlight in an unprecedented way, implying great opportunities, but also the need to communicate effectively, often amidst controversy.

A Machine Learning-Based Prediction of Hospital Mortality in Patients With Postoperative Sepsis.

Introduction: The incidence of postoperative sepsis is continually increased, while few studies have specifically focused on the risk factors and clinical outcomes associated with the development of sepsis after surgical procedures. The present study aimed to develop a mathematical model for predicting the in-hospital mortality among patients with postoperative sepsis.Materials and Methods: Surgical patients in Medical Information Mart for Intensive Care (MIMIC-III) database who simultaneously fulfilled Sepsis 3.0 and Agency for Healthcare Research and Quality (AHRQ) criteria at ICU admission were incorporated. We employed both extreme gradient boosting (XGBoost) and stepwise logistic regression model to predict the in-hospital mortality among patients with postoperative sepsis. Consequently, the model performance was assessed from the angles of discrimination and calibration.Results: We included 3,713 patients who fulfilled our inclusion criteria, in which 397 (10.7%) patients died during hospitalization, and 3,316 (89.3%) patients survived through discharge. Fluid-electrolyte disturbance, coagulopathy, renal replacement therapy (RRT), urine output, and cardiovascular surgery were important features related to the in-hospital mortality. The XGBoost model had a better performance in both discriminatory ability (c-statistics, 0.835 vs. 0.737 and 0.621, respectively; AUPRC, 0.418 vs. 0.280 and 0.237, respectively) and goodness of fit (visualized by calibration curve) compared to the stepwise logistic regression model and baseline model.Conclusion: XGBoost model has a better performance in predicting hospital mortality among patients with postoperative sepsis in comparison to the stepwise logistic regression model. Machine learning-based algorithm might have significant application in the development of early warning system for septic patients following major operations.

Development of Early Warning System for Public Health Emergencies in China: Focus on SARS, H7N9, COVID-19

Development of Early Warning System for Public Health Emergencies in China: Focus on SARS, H7N9, COVID-19

Mapping Forest Fire Risk and Development of Early Warning System for NW Vietnam Using AHP and MCA/GIS Methods

Forest fires constitute a major environmental problem in tropical countries, especially in the context of climate change and increasing human populations. This paper aims to identify the causes of frequent forest fires in Son La Province, a fire-prone and forested mountainous region in northwest Vietnam, with a view to constructing a forest fire-related database with multiple layers of natural, social and economic information, extracted largely on the basis of Landsat 7 images. The assessment followed an expert systems approach, applying multi-criteria analysis (MCA) with an analytical hierarchy process (AHP) to determine the weights of the individual parameters related to forest fires. A multi-indicator function with nine parameters was constructed to establish a forest fire risk map at a scale of 1:100,000 for use at the provincial level. The results were verified through regression analysis, yielding R2 = 0.86. A real-time early warning system for forest fire areas has been developed for practical use by the relevant government authorities to provide more effective forest fire prevention planning for Son La Province.

The Development of Early Warning Systems for Home/Community Elderly Care

With Taiwan now an "aged society", home safety for older individuals has become a very important issue. The purpose of establishing early warning systems in homes and/or communities is to generate and disseminate meaningful warning information to medical institutions or rescue units in a timely manner so that they may take timely and appropriate action. The main purpose of this paper is to introduce the current application of information and communication technology (ICT, especially the Internet of Things and artificial intelligence) in early warning systems for home and community care. Two approaches to developing these systems are introduced: instant detection and prevention monitoring. Instant detection facilitates fall detection and personnel tracking, while the focus of prevention monitoring is on preventing falls and physiological status monitoring. The challenges faced by in incorporating ICT into these early monitoring systems are discussed as well.