Early Warning Research Articles

"FLOODGUARD: An IoT-Powered Real-Time Flash Flood Monitoring and Forecasting System"

Abstract: In recent times, the surge in flooding incidents has become a pressing global issue, inflicting extensive damage on both lives and livelihoods. While floods remain an inevitable force of nature, their devastating impact can be alleviated. With the aid of cutting-edge technologies like the Internet of Things (IoT), proactive flood forecasting has become increasingly feasible. Through the seamless integration of IoT data, populations can receive early warnings and evacuate to safety, safeguarding both lives and valuables. A real-time solution is imperative, leveraging IoT data streams to deliver timely flood alerts. Our study presents an IoT-driven prototype designed to collect hydrological data from rivers, encompassing metrics such as water flow, level, and discharge, alongside meteorological parameters like temperature, humidity, wind speed, and direction. Employing a Long Short-Term Memory (LSTM) model, we analyze and classify collected data based on water discharge, level, rainfall, and temperature, predicting flood events as "no alert," "yellow alert," "orange alert," or "red alert." Given the dynamic nature of rivers, accurately measuring total water discharge poses challenges. To address this, we propose a novel methodology utilizing water flow, sectional area width, and average depth. Furthermore, we tackle the complexities associated with accurately measuring rainfall due to erratic weather conditions. Our system demonstrates robust performance in predicting flood event states, achieving high F1-scores of 97% for "no alert," 97% for "yellow alert," 96% for "orange alert," and 98% for "red alert.

Occupancy dynamics of free ranging American mink (Neogale vison) in Greece.

Identifying the environmental factors that determine the occurrence of invasive species is essential in defining and implementing effective control campaigns. Here, we applied multi-season occupancy models to analyze American mink (Neogale vison) track data collected using 121 floating rafts, as a function of factors occurring at multiple spatial scales. Our overall aim was to identify those factors that determine the use, colonization or abandonment of rafts by free ranging individuals found in western Macedonia, Greece. We found that increasing values of shrubs and rock cover at the micro-habitat scale were positively associated with the species' probability of raft use, as was the density of medium-sized rivers at the landscape scale. Colonization was found to increase with increasing amounts of shrub and reed cover; however, both variables were not informative. Conversely, the distance from the nearest fur farm was highly informative in predicting raft abandonment by the species. Effective control actions may require removal by trapping along rocky or densely vegetated riverbanks or lake shores located in the vicinity of the established fur farms in the area. Habitat management, although possible, may be difficult to implement due to the ability of the species to adapt. Finally, fur farms should maximize security and establish an early warning and rapid eradication system in case of future escapes.

Project Risk Analysis and Early Warning of Culture and Tourism Group Financial Leasing Company

Under the background of strict regulation, due to the cyclical adjustment of the financial leasing industry, some top companies have maintained a growth trend in asset size and net profit. Internal differentiation has emerged in the financial leasing industry, increasing operational pressure, and a slowing trend in asset size growth. Although the overall pressure on credit quality has eased, downward pressure on asset quality is widespread, and the recovery period for narrowing profit space is still long. With the continuous upgrading and iteration of digitization, intelligence, and specialization, Culture and Tourism Group Financial Leasing Company should strengthen its professional ethics, improve its risk analysis and prediction capabilities, design integrated business guidelines for due diligence information collection and risk analysis, rely on big data to build a leasing asset identification, evaluation, monitoring, and early warning system, and establish a comprehensive, multi angle, and lean risk analysis and early warning mechanism. Through case analysis, problems are identified in a timely manner, and continuous reflection and improvement are made. Business process diagrams are drawn according to different scenarios, crisis warning indicators are set up, and a lean risk management team is formed. Crisis warning signals are continuously tracked and released, and processes and asset quality management are continuously improved. A lean risk control system is established to reduce risk losses and create benefits for the enterprise.

Research and application of a flexible measuring array for deep displacement of landslides

Abstract. The multidimensional and multi-sliding surface measurement of deep-seated displacement on landslides poses a significant technical challenge in landslide monitoring and early warning. The fixed-borehole inclinometer serves as an important measurement method based on drilling for this purpose. In this study, a novel flexible measurement array for deep-seated landslide displacement and its installation and measurement processes were developed, enabling higher accuracy in full-hole multidimensional deformation measurement. The measurement array consists of individual measurement probes as basic units, connected in series through coaxial cables and high-pressure rubber hoses, forming a flexible measurement array. Each probe is equipped with acceleration and magnetic field sensors, allowing for the measurement of borehole inclination and azimuth angles and providing a more comprehensive understanding of the deformation of deep-seated landslides. This flexible measurement array resolves the limitations of traditional fixed inclinometers, such as limited probe quantity or inaccurate installation positions that fail to reflect the deformation trend of the landslide body. Moreover, it eliminates the need for auxiliary installation accessories like pulleys and inclinometer pipes, simplifying the mechanical structure and installation process, which represents an advancement in methodology and an improvement in measurement techniques. This array provides a more comprehensive and improved monitoring tool for disaster prevention and mitigation, thereby enhancing the level of geological hazard monitoring and early warning technology.

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.

A Study of Adaptive Threshold Based on the Reconstruction Model for Marine Systems and Their Equipment Failure Warning

To achieve the failure warning of marine systems and their equipment (MSAE), the threshold is one of the most prominent issues that should be solved first. In this study, a fusion model based on sparse Bayes and probabilistic statistical methods is applied to determine a new and more accurate adaptive alarm threshold. A multistep relevance vector machine (RVM) model is established to realize the parameter reconstruction in which the internal uncertainties caused by the degradation process and the external uncertainty caused by the loading, environment, and disturbances were considered. Then, a varying moving window (VMW) method is employed to determine the window size and achieve continuous data reconstruction. Further, the model based on Johnson distribution systems is utilized to complete the transformation of the residual parameters and calculate the adaptive threshold. Finally, the proposed adaptive decision threshold is successfully involved in the actual examples of the peak pressure and exhaust temperature of marine diesel engines. The results show that the proposed method can realize the continuous health condition monitoring of MSAE, successfully detect abnormal conditions in advance, achieve an early warning of failure, and reserve sufficient time for decision-making to prevent the occurrence of catastrophic disasters.

A newly early warning model for anaerobic digestion systems: Based on an improved sparrow search algorithm combined with least square support vector machine

Anaerobic digestion as an important means of organic waste treatment will play a key role in the realization of ecological civilization and the goal of double carbon. However, the instability of the system due to the high sensitivity to operating conditions restricts the economy and sustainability of the current commercial biogas projects in China. Machine learning as an early warning and control tool for many industrial systems is also applicable to anaerobic digestion systems. Existing studies focus on the biogas or methane yield prediction of the system, while there are few studies have considered the acid-bases indicators, which is crucial to the stability of the system. In this study, an improved sparrow search algorithm was developed, and after comparing its performance with selected optimization algorithms using CEC2017 test suite, combined with LSSVM, was applied to the prediction of eight different indicators of anaerobic systems, and eight datasets were validated. The results show that the optimization algorithm proposed in this study improves the performance of LSSVM and the model of ISSALSSVM shows excellent potential in the early warning and controlling of anaerobic digestion system.

Effect of Partial Substitution on Physical, Functional and Chemical Properties of Cookies Made from Mixture of Wheat and Coconut

Background: Since the closure of the wheat development company in Cameroon, all wheat is imported, but over the past year, the wheat prices have steadily increased in most of the countries' markets. The cost of a kilo, monitored by Famine Early Warning Systems Network (FEWS NET), fell from XAF 500 to XAF 600. With the use of composite flour, this will help to reduce the huge amount of money spent and lessen total dependence on imported wheat flour. Aim: The objective of this study was to produce cookies from composite flour made from coconut and wheat, and determine its nutritional properties and acceptability. Material and methods: Coconut flour was produced from fresh coconut using oven drying at standard conditions. The composite flour was formulated using the mixing plan design in Minitab 18.0. The functional properties of the flour and nutritional properties of the cookie samples were determined. Results: Results of functional properties show that the bulk density of CF1, CF2, CF3, CF4, and CF5 was 0.58, 0.50, 0.50, 0.46 and 0.47 g/L respectively; the pH was 6.42, 6.32, 6.37, 6.40, 6.46 respectively; swelling capacity was 49.10%, 44.07%, 42.02%, 46.23%, 47.09% respectively; water absorption capacity was 12%, 9%, 10.3%, 10.4%, 10% respectively; From this, the swelling capacity, water absorption and oil absorption capacity increased significantly with substitution (p < 0.05). The result of nutritional properties showed that the moisture was not affected by substitution in contrast to lipid, protein, fiber, carbohydrates, starch, amylose and amylopectin. The moisture, carbohydrate, starch and amylose decreased significantly (p < 0.05) with the substitution while ash, fat, protein and fiber increased. Results of the sensory analysis showed that the 85% wheat biscuits (CF3) had the highest scores in all the parameters evaluated and were better accepted than the other samples. Conclusion: These findings indicate that supplementation of cookies with coconut flour will improve the nutritional value of cookies and acceptability.

Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network

The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.

Bolt loosening angle detection through arrangement shape-independent perspective transformation and corner point extraction based on semantic segmentation results

The identification of loosened bolts is crucial for the early warning of structural damage and maintaining the overall stability of the structure. Most existing two-dimensional computer vision-based bolt loosening detection methods need to rely on the bolt arrangement shape for perspective transformation, to overcome this limitation, this study proposes a bolt loosening angle detection method through arrangement shape-independent perspective transformation and corner point extraction based on semantic segmentation results. First, a dataset of 1748 images containing bolts is collected. Second, a bolt region of interest (RoI) detection is developed to extract the sub-image of the bolt. The results show that the training and validation accuracy of bolt RoI detector are 99.5%, and 99.4%, respectively. Then, the contour corner points are extracted, the automatic and arrangement shape-independent perspective transformation of the image is completed using the planar homography-based image processing algorithm and the regular hexagonal characteristics of the bolt. Finally, automatic estimation of the bolt angle is performed on the corrected bolt image. The proposed method is used to detect bolt images obtained from different shooting distances, perspective angles, and lighting conditions. The results demonstrate that the method can accurately detect the looseness of bolts in bolt connections, and the quantification error is mostly less than 3°, it has the potential for real-time bolt loosening monitoring of multi-type bolted connections.

Real-time early warning and the prediction of air pollutants for sustainable development in smart cities

Air quality forecasting is vital for sustainable development in smart cities and is essential to protect public health through early warning against air pollutants. This article presents a novel ensemble deep learning method able to capture spatiotemporal features on short-term time intervals and extract high data abstraction levels. This method uses a hierarchical framework with a new activation function called HT-Tanh to improve the prediction of air pollutant emissions in real time. As such, a residual neural network (ResNet) is integrated with a convolutional neural network (CNN) to deeply extract the temporal and spatial features from pollutant and meteorological data. The model network encodes the fully connected layer to fine-tune the CNN output and decodes the temporal prediction relations based on gate recurrent unit (GRU) to obtain more accurate results. The reliability of the proposed method is verified through a case study from Shantou City, China. The model performance is compared with the recently developed and traditional deep learning models. Results reveal that the proposed method has good precision in the multiscale air quality predictions. Compared with CNN-GRU, convolutional long short-term memory, and CNN models, the forecasting results of the proposed method are significantly improved. The presented method in this article can support the early warning systems in multiple regions for air pollution modeling studies.

A novel useful marker in the early discrimination of transient hyperthyrotropinemia/hypothyroxinemia and congenital hypothyroidism in preterm infants: thyroid-stimulating hormone/free thyroxine ratio.

Transient hyperthyrotropinemia/transient hypothyroxinaemia and congenital hypothyroidism (CH) have completely different treatment and clinical outcomes. However, a powerful, highly sensitive and cost-effective marker for the differentiation of these clinical entities in the early postnatal period is not available. Therefore, we aimed to test the potential, early predictive, diagnostic power of the thyroid-stimulating hormone (TSH)/free thyroxine (fT4) ratio for differentiation of the two clinical entities in the early period of life. TSH and fT4 levels were recorded on the postnatal day 7 of premature infants<32weeks of gestational age. TSH/fT4 ratio was calculated. The significance degree of TSH/fT4 ratio was analyzed for the differentiation of transient hyperthyrotropinemia or transient hypothyroxinaemia and CH. The study included 1,204 preterm infants<32weeks of gestational age. Of the 1,204 infants, 978 (81.2 %) had normal thyroid function. Eighty-eight infants (7.3 %) were diagnosed with CH and 138 (11.5 %) with transient hyperthyrotropinemia or transient hypothyroxinemia. Initial TSH/fT4 ratio>4.8 was found to be an early diagnostic warning sign with high power in favor of transient hyperthyrotropinemia or transient hypothyroxinemia (AUC value: 0.947) and TSH/fT4 ratio>12.5 (AUC value: 0.999) was found to be an early diagnostic warning sign with high power in favor of CH (p=0.0001). We found for the first time that the TSH/fT4 ratio can be used for the early differentiation of transient hyperthyrotropinemia/transient hypothyroxinaemia and CH in preterm infants without additional cost and with high power.

Infection Control Through Clinical Pipelines Built with Arden Syntax MLM Building Blocks.

Healthcare-associated infections (HAIs) may have grave consequences for patients. In the case of sepsis, the 30-day mortality rate is about 25%. HAIs cost EU member states an estimated 7 billion Euros annually. Clinical decision support tools may be useful for infection monitoring, early warning, and alerts. MONI, a tool for monitoring nosocomial infections, is used at University Hospital Vienna, but needs to be clinically and technically revised and updated. A new, completely configurable pipeline-based system for defining and processing HAI definitions was developed and validated. A network of data access points, clinical rules, and explanatory output is arranged as an inference network, a clinical pipeline as it is called, and processed in a stepwise manner. Arden-Syntax-based medical logic modules were used to implement the respective rules. The system was validated by creating a pipeline for the ECDC PN5 pneumonia rule. It was tested on a set of patient data from intensive care medicine. The results were compared with previously obtained MONI output as a suitable reference, yielding a sensitivity of 93.8% and a specificity of 99.8%. Clinical pipelines show promise as an open and configurable approach to graphically-based, human-readable, machine-executable HAI definitions.

Tsunami Response in Batu Bengkung Beach Tourism, Gajahrejo Village, Malang Regency

Batu Bengkung Beach in Malang Regency has excellent potential as a natural tourist destination. However, the natural condition which is an area prone to tsunami disasters also cannot be ignored. Safety considerations and minimizing casualties are the main reasons this tour aims to make adjustments to aspects of tsunami disaster mitigation. Likewise for the sustainability of tourism in the future. The method carried out is through qualitative and quantitative data research. Direct observation at the location is also needed to determine exactly the existing conditions, as well as interviews with several resource persons as data reinforcement. The result is the application of aspects of tsunami disaster mitigation that are relevant to existing natural conditions. Aspects of tsunami disaster mitigation that can be applied are, first, the determination or determination of evacuation locations and evacuation routes. By utilizing natural conditions surrounded by hills, these conditions can be an ideal evacuation location. The second early warning is in the form of an alarm or siren warning of a tsunami. Aims to notify of the tsunami disaster and is expected to evacuate immediately. The third erected vertical evacuation buildings in the form of tsunami shelters that met FEMA guideline standards. And finally regular disaster mitigation simulation training.

Optimal timing for the Modified Early Warning Score for prediction of short-term critical illness in the acute care chain: a prospective observational study

IntroductionThe Modified Early Warning Score (MEWS) is an effective tool to identify patients in the acute care chain who are likely to deteriorate. Although it is increasingly being implemented in...

Experimental Study on Enhanced Methane Detection Using an MEMS-Pyroelectric Sensor Integrated with a Wavelet Algorithm.

An optical sensing approach that balances portability with cost efficiency has been designed for the reliable monitoring of fugitive methane (CH4) emissions. Employing a LiTaO3-based pyroelectric detector integrated with micro-electro-mechanical systems and a broad infrared source, the developed gas sensor adeptly measured CH4 concentrations with a low limit of detection of about 5.6 ppmv and showed rapid response times with t90 consistently under 3 s. Notably, the novelty of our method lies in its precise control and reduction of CH4 levels, enhanced by wavelet denoising. This technique, optimized through meticulous grid search, effectively mitigated noise interference noticeable at CH4 levels below 10 ppmv. Postdenoising, nonlinear regression analyses based on the modified Beer-Lambert equation returned R2 values of 0.985 and 0.982 for the training and validation sets, respectively. In conclusion, this gas sensor has been shown to be able to meet the requirements for early warning of CH4 leakage on the surface in various carbon capture, utilization, and storage projects such as enhanced oil or gas recovery projects using CO2 injection.

Hazard warning modalities and timing thresholds for older drivers with impaired vision

PurposeWe examined collision warning systems with different modalities and timing thresholds, assessing their impact on responses to pedestrian hazards by drivers with impaired contrast sensitivity (ICS). MethodsSeventeen ICS (70–84 y, median CS 1.35 log units) and 17 normal vision (NV: 68–73 y, median CS 1.95) participants completed 6 city drives in a simulator with 3 bimodal warnings: visual-auditory, visual-directional-tactile, and visual-non-directional-tactile. Each modality had one drive with early and one with late warnings, triggered at 3.5 s and 2 s time-to-collision, respectively. ResultsICS participants triggered more early (43 vs 37 %) and late warnings (12 vs 6 %) than NV participants and had more collisions (3 vs 0 %). Early warnings reduced time to fixate hazards (late 1.9 vs early 1.2 s, p < 0.001), brake response times (2.8 vs 1.8 s, p < 0.001) and collision rates (1.2 vs 0.02 %). With late warnings, ICS participants took 0.7 s longer to brake than NV (p < 0.001) and had an 11 % collision rate (vs 0.7 % with early warnings). Non-directional-tactile warnings yielded the lowest collision rates for ICS participants (4 vs auditory 12 vs directional-tactile 15.2 %) in late warning scenarios. All ICS participants preferred early warnings. ConclusionsWhile early warnings improved hazard responses and reduced collisions for ICS participants, late warnings did not, resulting in high collision rates. In contrast, both early and late warnings were helpful for NV drivers. Non-directional-tactile warnings were the most effective in reducing collisions. The findings provide insights relevant to the development of hazard warnings tailored for drivers with impaired vision.

Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Management

With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve real-time monitoring and early warning, helping banks intervene before risks occur and reduce losses.

Early earthquake warning for hospital preparedness: Safeguarding vulnerable populations in Mexico city

While some literature exists on the necessity of Early Earthquake Warning (EEW) for the general population, establishing its usefulness in high-risk environments like working with hazardous materials, machines, and in operating rooms, little is known about the relevance of this tool in safeguarding vulnerable people. Existing studies on the Mexican population's reactions to EEW have shown that people are likely not to differentiate their protective actions in response to an EEW or ground motion without an EEW. However, it is not possible to ascertain the same for specific population segments, such as healthcare professionals with responsibilities for individuals with physical vulnerabilities (i.e., hearing, mobility, vision, among others). This study explores the use of protocols in relation to EEW in Mexico City's hospitals, comparing them to the baseline results from three hospitals in the US, with the purpose to better understand the pertinence of the warning. In-depth interviews were conducted with hospitals' civil protection officials, staff, and representatives responsible for EEW provision and maintenance. Our findings indicate that the capacity (financial and human) to train and continuously perform drills is a limiting factor for protocol adoption, as well as the lack of clarity on where within the hospital the EEW should be located, how it should emit the warning, and who should do what in response. The challenges identified by the interviewees in Mexico and the US are very similar, while the practical employment of the EEW differs vastly.

Macro-financial imbalances and cyclical systemic risk dynamics: understanding the factors driving the financial cycle in the presence of non-linearities

Abstract This paper develops a multivariate filter based on an unobserved component model to estimate the financial cycle. Our model features: (1) a dynamic relationship between the financial cycle and key variables; (2) time-varying shock volatility for trend and cycle components. We demonstrate that our approach not only exhibits superior early warning properties for banking crises but also outperforms commonly used indicators in terms of data fit for decomposition exercises, as evidenced by the higher marginal likelihood. We document three important properties of the financial cycle. First, the sensitivity of the financial cycle to changes in real estate valuations increased during the post-90s period. Second, the sensitivity of the cycle to changes in financial conditions displays volatility and country specificities. Finally, our reduced form estimates suggest that the banking crisis of 1988 was preceded by positive contributions from the risk appetite shock, while the primary source of vulnerabilities emanated from the housing market in the run-up to the Global Financial Crisis.