Development Of Warning System Research Articles

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

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.

Development of an early warning system for higher education institutions by predicting first‐year student academic performance

AbstractIn this study, an early warning system predicting first‐year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first‐year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental analyses includes 11,698 freshman students' data. The problem is constructed as classification models predicting whether a student will be successful or unsuccessful at the end of the first year. A total of 69 input variables are utilized in the models. Naive Bayes, decision tree and random forest algorithms are compared over model prediction performances. Random forest models outperformed others and reached 90.2% accuracy. Findings show that the models including the fall semester CGPA variable performed dramatically better. Moreover, the student's programme name and university placement exam score are identified as the other most significant variables. A critical discussion based on the findings is provided. The developed model may be used as an early warning system, such that necessary actions can be taken after the second week of the spring semester for students predicted to be unsuccessful to increase their success and prevent attrition.

Composition of factors for local heat adaptation measures at the local level in cities of the mid-latitude – An approach for the south-west of Germany

Traditional heat health warning systems focus on severe and extreme heat events at the district or regional level, often overlooking localized risk and protective factors such as healthcare access and urban green spaces. This approach considers less the varying impacts of heat within cities, including the phenomenon of Urban Heat Islands (UHIs) and the diverse needs of different populations. To address these shortcomings, a need for the development of an Urban Heat Health Warning and Information System (UHHWIS) that operates within the framework of Heat Health Action Plans is needed. Such a system integrates national acute heat health warnings with city-specific assessments of UHI effects and other relevant factors. The technical implementation of the UHHWIS involves the calculation and preprocessing of basic factors such as the Normalised Difference Vegetation Index (NDVI), imperviousness, and UHI intensity. Additionally, further factors are assessed, spatially processed, and provided in accordance with Open Geospatial Consortium (OGC) standards. An iso-area analysis is conducted to evaluate the accessibility of protective factors, such as urban green spaces, drinking wells, hospitals, physicians, and pharmacies, based on the city’s road topology. One crucial factor considered in the system is the casting of shadows, which is influenced by both time and location and facilitated through deck.gl. The developed template encompasses all these components into a unified system aimed at protecting vulnerable and risk groups, such as the elderly, through resilient, climate-adapted urban planning. The system provides warnings and information tailored to the urban morphology and prevailing conditions, complemented by a catalogue of potential short- to long-term measures focused on behavioral changes and climate-resilient urban planning strategies. The template can be adapted for use in various European cities, offering valuable insights to decision-makers in city administration for mitigating thermal stress and enhancing resilience against urban heat nowadays and in future.

Development of Athlete Health Monitoring and Early Warning System Based on Sensor Data

An athlete health monitoring and early warning system based on sensor data utilizes wearable sensors to track various physiological parameters, such as heart rate, body temperature, oxygen saturation, and movement patterns. These sensors continuously collect real-time data during training sessions and competitions. Machine learning algorithms analyze the data to detect deviations from normal patterns and identify potential indicators of injury, fatigue, or overtraining. By monitoring athletes' health parameters over time and comparing them to established baseline values, the system can provide early warnings of potential health issues or performance declines. Coaches and medical staff can then intervene promptly with appropriate interventions, adjustments to training loads, or rest periods to prevent injuries and optimize athletes' performance and well-being. . The paper introduces the Reliable Fuzzy Sensor Optimized Automated Monitoring (RFSO-DL) system, a novel approach to athlete health monitoring leveraging fuzzy logic and sensor data optimization techniques. RFSO-DL offers a comprehensive solution for real-time assessment of athlete well-being by analyzing physiological measurements and activity levels. Through a series of experiments, the system demonstrates high accuracy, precision, recall, and F1 score in classifying athlete health status. Results indicate RFSO-DL's effectiveness in capturing subtle variations in health status and providing timely interventions. Through experiments, the system achieves impressive results, with accuracy ranging from 90.5% to 94.1%, precision from 85.8% to 91.2%, recall from 91.7% to 95.6%, and F1 score from 88.6% to 93.3%. These findings underscore RFSO-DL's potential to revolutionize athlete health monitoring by accurately classifying health status based on physiological measurements and activity levels.

Development of an Early Warning System Using Social Media for Flood Disaster

Development of an Early Warning System Using Social Media for Flood Disaster

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.

Developing an Australian dust early warning system

In Australia, the perception of large continental-scale dust storms has traditionally been restricted to aspects of ‘inconvenience’ or ‘novelty’. However, as the climate changes, there is an increased likelihood of dust storm activity that puts communities at increased risk. The aim of this study was to explore the need and possibility of developing a dust early warning system for Australia. Through a scoping review of internationally used sand and dust early warning systems, we found that an array of systems exist or were theoretically tested in the Northern Hemisphere. The sensor networks, one of which was operational, were of particular interest because Australia already has an operational air quality network that could be expanded to a dust early warning system. The need for a trans-boundary system is critical for Australia and, therefore, requires a combined approach of expanded sensor network with satellite-based systems to increase validation of any future modelling approach. This improved understanding could inform the development of a dust warning system for multi-socio-economic factors that account for cumulative exposure to small, localised and large continental-sized dust events.

Development of a Bayesian network-based early warning system for storm-driven coastal erosion

Coastal hazards such as flooding and erosion can cause large economic and human losses. Under this threat, early warning systems can be very cost-effective solutions for disaster preparation. The goal of this study was to develop, test, and implement an operational coastal erosion early warning system supported by a particular method of machine learning. Thus, the system combines Bayesian Networks, and state-of-the-art numerical models, such as XBeach and SWAN, to predict storm erosion impacts in urbanized areas. This system was developed in two phases. In the development phase, all information required to apply the machine learning method was generated including the definition of hundreds of oceanic synthetic storms, modeling of the erosion caused by these storms, and characterization of the impact levels according to a newly defined eerosion iimpact index. This adimensional index relates the distance from the edge of the dune/beach scarp to buildings and the height of that scarp. Finally, a Bayesian Network that acted as a surrogate of the previously generated information was built. After the training of the network, the conditional probability tables were created. These tables constituted the ground knowledge to make the predictions in the second phase. This methodology was validated (1) by comparing 6-h predictions obtained with the Bayesian Network and with process-based models, the latest considered as the benchmark, and (2) by assessing the predictive skills of the Bayesian Network through the unbiased iterative k-fold cross-validation procedure. Regarding the first comparison, the analysis considered the entire duration of three large storms whose return periods were 10, 16, and 25 years, and it was observed that the Bayesian Network correctly predicted between 64% and 72% of the impacts during the course of the storms, depending on the area analyzed. Importantly, this method was also able to identify when the hazardous conditions disappeared after predicting potential consequences. Regarding the Regarding the second validation approach, second validation approach, the k-fold cross-validation procedure was applied to the peak of a set of varying storms and it demonstrated that the predictive skills were maximized (63%–72%) when including three nodes as input conditions of the Bayesian Network. In the operational phase, the system was integrated into the architecture of a forecast and early warning system that predicts emergencies in coastal and port zones in Portugal, and the alerts are issued to authorities every day. This study demonstrated that the two-phase approach developed here can provide fast and high-accuracy predictions of erosion impacts. Also, this methodology can be easily implemented on other sandy beaches constituting a powerful tool for disaster management.

Development and application of early warning system for heavy metal pollution accident in drinking water source area

With increasing extreme weather events and urban development, drinking water source areas (DWSAs) face rising water quality pollution incidents. Timely and precise pollution incident early warning is critical. This study integrated the hydrodynamic model and water quality early warning model developed for the Three Gorges Reservoir Area (TGRA)'s DWSA, establishing a water pollution incident early warning system for the TGRA. Assuming a heavy metal zinc (Zn) pollution incident in the TGRA's Heshangshan drinking water source area (HDWSA), the study analyzed pollutant Spatio-temporal variations and the duration of early warning exceedance during different water periods. Results indicated varying durations for pollutant levels to return to background values across the four water periods: 123 min (dry), 115 min (falling), 106 min (storage), and 96 min (flood). Level I early-warning durations ranged from long to short: 68 min (dry), 63 min (falling), 57 min (storage), and 50 min (flood), respectively. The early warning system is scientifically reliable and supports decision-making for DWSA water pollution incidents.

Artificial Intelligence and Machine Learning tools for improving Early Warning systems of volcanic eruptions: the case of Stromboli.

Explosive volcanic blasts can occur suddenly and without any clear precursors. Many volcanoes have erupted in the last years with no evident change in the eruptive parameters and with dramatic consequences for the population living nearby the volcano and the tourists visiting the active areas. In recent years, a big effort has been made to develop Early Warning systems to issue timely alerts to the population. At Stromboli volcano, the development of sensitive instruments to measure the deformation (tilt) of the ground has revealed that the volcano edifice is inflating tens of minutes before the explosion following a recurrent exponential ramp-like pattern. This scale-invariant of ground deformation has allowed the development of a quasi-deterministic Early Warning system which is operative since 2019. In this article we show how Artificial Intelligence and Machine Learning can be successfully applied to improve the efficiency and the sensitivity of Early Warning systems, provided the availability of a comprehensive experimental data set on past explosive events. The approach presented here for the Stromboli case demonstrates promising results also in forecasting the intensity of explosive events, offering valuable insights and new perspectives into the potential risks associated with volcanic activities.

Research and development of an intrusion warning system using advanced artificial intelligence algorithms

Given the extremely complicated problems of theft, having an intrusion warning system, especially at construction sites, is extremely urgent. In this study, we will build an automatic intrusion warning system when someone enters an area on a construction site automatically and accurately. Specifically, we used advanced deep learning models such as YOLOv5 and YOLOv8 to obtain the coordinates of the object and then compared them with the coordinates of the monitored area to determine whether the conduct was a violation. The results achieved in this study were very good when the YOLOv5n model achieved an average accuracy of more than 91% with a sensitivity of more than 84% and a processing speed of more than 12 frames per second, similar to the YOLOv8n model that achieved an average accuracy of more than 92%, with a sensitivity of more than 82% and a processing speed of more than 15 frames per second.

Development of an Early Warning System to Support Educational Planning Process by Identifying At-Risk Students

Development of an Early Warning System to Support Educational Planning Process by Identifying At-Risk Students

Identifying heat thresholds for South Africa towards the development of a heat-health warning system.

Exposure to heatwaves may result in adverse human health impacts. Heat alerts in South Africa are currently based on defined temperature-fixed threshold values for large towns and cities. However, heat-health warning systems (HHWS) should incorporate metrics that have been shown to be effective predictors of negative heat-related health outcomes. This study contributes to the development of a HHWS for South Africa that can potentially minimize heat-related mortality. Distributed lag nonlinear models (DLNM) were used to assess the association between maximum and minimum temperature and diurnal temperature range (DTR) and population-adjusted mortality during summer months, and the effects were presented as incidence rate ratios (IRR). District-level thresholds for the best predictor from these three metrics were estimated with threshold regression. The mortality dataset contained records of daily registered deaths (n = 8,476,532) from 1997 to 2013 and data for the temperature indices were for the same period. Maximum temperature appeared to be the most statistically significant predictor of all-cause mortality with strong associations observed in 40 out of 52 districts. Maximum temperature was associated with increased risk of mortality in all but three of the districts. Our results also found that heat-related mortality was influenced by regional climate because the spatial distribution of the thresholds varied according to the climate zones across the country. On average, districts located in the hot, arid interior provinces of the Northern Cape and North West experienced some of the highest thresholds compared to districts located in temperate interior or coastal provinces. As the effects of climate change become more significant, population exposure to heat is increasing. Therefore, evidence-based HHWS are required to reduce heat-related mortality and morbidity. The exceedance of the maximum temperature thresholds provided in this study could be used to issue heat alerts as part of effective heat health action plans.

Emerging opportunities for wildlife conservation with sustainable autonomous transportation

Autonomous vehicles (AVs) are expected to play a key role in the future of transportation, and to introduce a disruptive yet potentially beneficial change for wildlife–vehicle interactions. However, this assumption has not been critically examined, and reducing the number of wildlife–vehicle collisions (WVCs) may be beyond current technological capabilities. Here, we introduce a conceptual framework encompassing the intersection between AV technology and wildlife conservation to reduce WVCs. Our proposed framework integrates the development of robust warning systems and animal detection methods for AV systems, and the incorporation of wildlife–vehicle interactions into decision‐making algorithms. With large‐scale AV deployment a looming reality, it is vital to incorporate conservation and sustainability into the societal, ethical, and legal implications of AV technology. We intend our framework to help ecologists and conservationists foster the necessary interdisciplinary collaborations with AV developers and policy makers to reduce WVCs and concomitant biodiversity loss.

Quantifying climatic and socioeconomic drivers of urban malaria in Surat, India: a statistical spatiotemporal modelling study

Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India. We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk. The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model. This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts. US National Institutes of Health.

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data

Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system.

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.

Integrating “Top-Down” and “Community-Centric” Approaches for Community-Based Flood Early Warning Systems in Namibia

The United Nations Office for Disaster Risk Reduction (UNDRR) resolutely endorses the development of people-centred early warning systems. Moreover, several scientific studies have deprecated the vast technology-driven technocratic approaches to implementing these systems, contending the prioritisation of communities and their contextualised needs for systems that ensure effective risk protection and resilience building. However, both the UNDRR and the scientific literature have failed to define appropriate means of implementation (MoI) for community-based early warning system (CBEWS) development. Floods are a major hazard annually impacting several communities in rural sub-Saharan Africa, and though several opportunities for participatory systems have been identified, investment in developing countries is often lagging, and no defined mechanism for developing these systems exists. Adopting a modified Integrated Emergency Management Framework, this study demonstrates that an impact-based CBEWS can be established by leveraging existing resources, institutions and stakeholders, essentially merging last-mile and first-mile approaches. The study further reveals that directly linking technical capacities with community-based efforts allows communities to define system development parameters, strengthen risk knowledge and response, and build resilience for improved physical, economic and environmental protection, essentially bridging the gap between first and last-mile approaches. The study also highlights the need for governments to appropriately streamline DRR to improve coordination and communication.