ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Early warning systems for enteral feeding intolerance in patients with stroke

Objective:The objective of this study was to construct an early warning system(EWS)to facilitate risk assessment,early identification,and appropriate treatment of enteral nutrition feeding intolerance(FI)in patients with stroke,so as to provide a reference for risk classification standards and interventions toward a complete EWSs for nursing care of stroke.Materials and Methods:Based on evidence and clinical nursing practice,a structured expert consultation method was adopted on nine experts over two rounds of consultation.Statistical analysis was used to determine the early warning index for FI in patients with stroke.Results:The expert authority coefficient was 0.89;the coefficients of variation for the two rounds of consultation were 0.088-0.312 and 0.096-0.214,respectively.There were significant differences in the Kendall’s concordance coefficient(P<0.05).Finally,22 items in five dimensions of patient age,disease,treatment,biochemical,and enteral nutrition-related factors were identified.Conclusion:The early warning index for FI in patients with a history of stroke is valid and practical.It provides a reference for the early clinical identification of FI risk.

The SNAKE System: CEMADEN’s Landslide Early Warning System (LEWS) Mechanism

In Brazil, the prominent climate-induced disasters are floods and mass movements, with the latter being the most lethal. The spate of major landslide events, especially those in 2011, catalyzed the creation of CEMADEN (National Center for Monitoring and Early Warning of Natural Disasters). This article introduces one of CEMADEN’s pivotal systems for early landslide warnings and traces its developmental timeline. The highlighted SNAKE System epitomizes advancements in digital monitoring, forecasting, and alert mechanisms. By leveraging precipitation data from pluviometers in observed municipalities, the system bolsters early warnings related to potential mass movements, like planar slides and debris flows. Its deployment in CEMADEN’s Situation Room attests to its suitability for overseeing high-risk municipalities, attributed primarily to its robustness and precision.

Construction and Optimization of a Financial Early Warning System Based on Big Data and Deep Learning Technology

New technologies such as big data,artificial intelligence,mobile internet,cloud computing,Internet of Things,and blockchain have brought about significant changes and development in the financial industry.Predicting the financial situation of enterprises,reducing the probability of uncertainty risks,and reducing the likelihood of financial crises have become important issues in enterprise financial crisis warning.In view of the issues in enterprise financial early warning systems such as lag,low accuracy,and high warning costs in data analysis,a financial early warning system based on big data and deep learning technology has been established,taking into account the different situations of listed and non-listed companies.This carries significance in improving the accuracy of enterprise financial early warning and promoting timely and effective decision-making.

Application of the monitoring and early warning system for internal solitary waves:Take the second natural gas hydrates production test in the South China Sea as an example

Internal solitary waves(ISWs) contain great energy and have the characteristics of emergency and concealment. To avoid their damage to offshore engineering, a new generation of monitoring and early warning system for ISWs was developed using technologies of double buoys monitoring, intelligent realtime data transmission, and automatic software identification. The system was applied to the second natural gas hydrates(NGHs) production test in the Shenhu Area, South China Sea(SCS) and successfully provided the early warning of ISWs for 173 days(from October 2019 to April 2020). The abrupt changes in the thrust force of the drilling platform under the attack of ISWs were consistent with the early warning information, proving the reliability of this system. A total of 93 ISWs were detected around the drilling platform. Most of them occurred during the spring tides in October–December 2019 and April 2020, while few of them occurred in winter. As suggested by the theoretical model, the full-depth structure of ISWs was a typical current profile of mode-1, and the velocities of wave-induced currents can reach 80 cm/s and30 cm/s, respectively, in the upper ocean and near the seabed. The ISWs may be primarily generated from the interactions between the topography and semidiurnal tides in the Luzon Strait, and then propagate westward to the drilling platform. This study could serve as an important reference for the early warning of ISWs for offshore engineering construction in the future.

Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System

Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including inherent characteristics of cognitive radio(CR)are potential candidates to develop a monitoring and early warning system(MEWS)that helps in efficiently utilizing the short response time to save lives during flash floods.However,most CIoT devices are battery-limited and thus,it reduces the lifetime of the MEWS.To tackle these problems,we propose a CIoTbased MEWS to slash the fatalities of flash floods.To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors,we formulate a resource assignment problem for maximizing energy efficiency.To solve the problem,at first,we devise a polynomial-time heuristic energyefficient scheduler(EES-1).However,its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency.To enhance the energy efficiency of the proposed EES-1 scheme,we additionally formulate an optimization problem based on a maximum weight matching bipartite graph.Then,we additionally propose a Hungarian algorithm-based energy-efficient scheduler(EES-2),solvable in polynomial time.The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS,leading to the extended lifetime of the MEWS without loss of throughput performance.

Development of community-based landslide early warning system in the earthquake-affected areas of Nepal Himalaya

In the central Nepal Himalaya,landslides form the major natural hazards annually resulting in many casualties and damage.Structural as well as non-structural measures are in place to minimize the risk of landslide hazard.To reduce the landslide risk,a Landslide Early Warning System(LEWS)as a nonstructural measure has been piloted at Sundrawati village(Kalinchowk rural municipality,Dolakha district)to identify its effectiveness.Intensive discussions with stakeholders,aided by landslide susceptibility map,resulted in a better understanding of surface dynamics and the relationship between rainfall and surface movement.This led to the development of a LEWS comprised of extensometers,soil moisture sensors,rain gauge stations,and solar panels as an energy source that blows siren receiving signals via a micro-controller and interfacing circuit.The data generated through the system is transmitted via a Global System for Mobile Communications(GSM)network to responsible organizations in realtime to circulate the warning to local residents.This LEWS is user-friendly and can be easily operated by a community.The successful pilot early warning system has saved 495 people from 117 households in August 2018.However,landslide monitoring and dissemination of warning information remains a complex process where technical and communications skill should work closely together.

Overview of an early warning system for Glacial Lake outburst flood risk mitigation in Dudh-Koshi Basin, Nepal

Natural disasters inflict severe damage on almost the entire spectrum of social and natural habitats. This ranges from housing and shelter, water, food, health, sanitation to information and communication networks, supply of power and energy,transportation infrastructure, and others. Nepal is a risk prone country for Glacial Lake Outburst Flood(GLOF). GLOFs exist as major challenges as they repeatedly cause a heavy toll of life and property. During such a disaster, major challenges are indeed the protection of life, property and vital life-supporting infrastructure. Any delay or laxity in disaster relief can escalate the magnitude of distress for the victims. Thus, rather than trying to take curative measures, it is better to minimize the impacts of GLOF. These measures subsequently help in reducing the magnitude of death and casualties due to a GLOF event. This reduction of impact is often achieved by optimizing preventive measures. For applying necessary deterrent measures, it is essential to disseminate information about the danger beforehand. Early Warning System(EWS) is an important step for such information dissemination for GLOF disaster management and helps to anticipate the risk of disaster and disseminate information to lives at risk. It is impossible and impractical to reduce all GLOF risks, but it is possible to reduce several impacts of a GLOF through the implementation of the EWS. This paper presents the design and implementation of an EWS for monitoring potential outbursts of a glacier lake in the Dudh-Koshi Basin, Nepal.

Preface to the special issue on Earthquake early warning system and rapid seismic instrumental intensity report

In the past several years, from May 12, 2008 Wenchuan Mw8.0 earthquake in China to March 11, 2011 off the Pacific coast of Northeastern Mw9.0 earthquake in Japan, the world witnessed catastrophic disasters caused by destructive earthquakes. The earthquake posed a great threat to the development of society and economy, especially in the developing countries such as China. In order to reduce the losses in peoples life and properties in maximum possibilities, there were a lots of technologies had been researched and developed, among them the earthquake early warning system （EEWS） and rapid seismic instrumental intensity report （RSIIP） are the two of the state-of-the-art technologies for the purpose. They may be used to minimize property damage and loss of life and to aid emergency response after a destructive earthquake.

Research on the Early Warning System of Cold Chain Cargo Based on OCR Technology

This paper designs a set of semi-automatic intelligent cold chain cargo proximity warning system with wireless data transmission, lightweight Optical Character Recognition identification algorithm framework and electronic label automatic warning as the core technology for cold chain dairy Fast Moving Consumer Goods contractors. In terms of hardware, Pulse Frequency Modulation modulation and demodulation are used as the main technology to realize wireless transmission and reception of equipment, and digital electronic tags are added to warn the same batch of upcoming goods. In terms of software, based on Chinese-ocr algorithm, image preprocessing and recognition methods are studied, and an early warning system is designed. So as to realize semi-automatic early warning of cold chain logistics goods.

An Early Warning System for Curved Road Based on OV7670 Image Acquisition and STM32

Nowadays,the number of vehicles in China has increased significantly.The increase of the number of vehicles has also led to the increasingly complex traffic situation and the urgent safety measures in need.However,the existing early warning devices such as geomagnetic,ultrasonic and infrared detection have some shortcomings like difficult installation and maintenance.In addition,geomagnetic detection will damage the road surface,while ultrasonic and infrared detection will be greatly affected by the environment.Considering the shortcomings of the existing solutions,this paper puts forward a solution of early warning for vehicle turning meeting based on image acquisition and microcontrollers.This solution combines image acquisition and processing technology,which uses image sensor to perceive traffic condition and image data analysis algorithm to process perceived image,and then utilize LED display screen to issue an early warning.

Earthquake magnitude estimation using the s_c and P_d method for earthquake early warning systems

Earthquake early warning （EEW） systems are one of the most effective ways to reduce earthquake disaster. Earthquake magnitude estimation is one of the most important and also the most difficult parts of the entire EEW system. In this paper, based on 142 earthquake events and 253 seismic records that were recorded by the KiK-net in Japan, and aftershocks of the large Wenchuan earthquake in Sichuan, we obtained earthquake magnitude estimation relationships using the τe and Pa methods. The standard variances of magnitude calculation of these two formulas are ±0.65 and ±0.56, respectively. The Pd value can also be used to estimate the peak ground motion of velocity, then warning information can be released to the public rapidly, according to the estimation results. In order to insure the stability and reliability of magnitude estimation results, we propose a compatibility test according to the natures of these two parameters. The reliability of the early warning information is significantly improved though this test.

Construction of Forecast and Early Warning System of Meteorological and Geological Disasters in Qinghai Province

Based on the meteorological and geological disaster data, ground observation data set, CLDAS grid point data set, and EC, BJ and other model product data during 2008-2020, the temporal and spatial distribution characteristics of meteorological and geological disasters and precipitation were analyzed, and the causes of the occurrence of meteorological geological disasters and the deviation of model precipitation forecast were revealed. Besides, an objective precipitation forecast system and a forecast and early warning system of meteorological and geological disasters were established. The results show that meteorological and geological disasters and precipitation were mainly concentrated from May to October, of which continuous precipitation appeared frequently in June and September, and convective precipitation was mainly distributed in July-August;the occurrence frequency of meteorological and geological disasters was basically consistent with the distribution of accumulated precipitation and short-term heavy precipitation, and they were mainly concentrated in the southern and eastern parts of Qinghai. Meteorological and geological disasters were basically caused by heavy rain and above, and meteorological and geological disasters were divided into three types: continuous precipitation(type I), short-term heavy precipitation(type II) and mixed precipitation(type III). For type I, the early warning conditions of meteorological and geological disasters in Qinghai are as follows: if the soil volumetric water content difference between 0-10 and 10-40 cm is ≤0.03 mm^(3)/mm^(3), or the soil volumetric water content at one of the depths is ≥0.25 mm^(3)/mm^(3), the future effective precipitation reaches 8.4 mm in 1 h, 10.2 mm in 2 h, 11.5 mm in 3 h, 14.2 mm in 6 h, 17.7 mm in 12 h, and 18.2 mm in 24 h, and such warning conditions are mainly used in Yushu, Guoluo, southern Hainan, southern Huangnan and other places. For type II, when the future effective precipitation is up to 11.5 mm in 1 h, 14.9 mm in 2 h, 16.2 mm in 3 h, 19.9 mm in 6 h, 25.3 mm in 12 h, and 26.3 mm in 24 h, such precipitation thresholds are mainly used in Hainan, Huangnan, and eastern Guoluo;as it is up to 13.3 mm in 1 h, 15.5 mm in 2 h, 16.6 mm in 3 h, 19.9 mm in 6 h, 31.1 mm in 12 h, and 34.0 mm in 24 h, such precipitation thresholds are mainly used in Hehuang valley. The precipitation thresholds of type III are between type I and type II, and closer to that of type II;such precipitation thresholds are mainly used in Hainan, Huangnan, and northern Guoluo. The forecasting ability of global models for heavy rain and above was not as good as that of mesoscale numerical prediction model, and global models had a wet bias for small-scale precipitation and a dry bias for large-scale precipitation;meso-scale models had a significantly larger precipitation bias. The forecast ability of precipitation objective forecast system constructed by frequency matching and multi-model integration has improved. At the same time, the constructed grid forecast and early warning system of meteorological and geological disasters is more precise and accurate, and is of instructive significance for the forecast and early warning of meteorological and geological disasters.

Progress of the earthquake early warning system in Fujian, China

In this article, we systematically introduce the atest progress of the earthquake early warning （EEW） ;ystem in Fujian, China. We focus on the following key echnologies and methods： continuous earthquake location md its error evaluation; magnitude estimation; reliability udgment of EEW system information; use of doubleparameter principle in EEW system information release hreshold; real-time estimation of seismic intensity and available time for target areas; seismic-monitoring network and data sharing platform; EEW system information ; elease and receiving platform; software test platform; and est results statistical analysis. Based on strong ground notion data received in the mainshock of the Wenchuan earthquake, the EEW system developed by the above algorithm is simulated online, and the results show that the ;ystem can reduce earthquake hazards effectively. In lddition, we analyzed four earthquake cases with magniude greater than 5.5 processed by our EEW system since he online-testing that was started one year ago, and results ndicate that our system can effectively reduce earthquake lazards and have high practical significance.

Design and Availability Research of a Flammable and Explosive Volatiles Monitoring and Early Warning System (FEVMEW) for the Bus Crowded Places

In order to reduce the arson or accidental fire losses, we developed a gas sensitive detector used for the rapid detection and early warning of flammables in crowded places such as buses. A MEMS (Micro-Electro-Mechanical System) based thin film semiconductor was fabricated as the gas sensor. To obtain the target gas selective response, the surface of the sensitive film was modified with highly active metal catalytic nano-particles. Thus the anti-interference ability was improved and the false alarm rate was effectively reduced. Furthermore, the modular embedded system for information acquisition and transmission was developed. Supported by the Airflow Precision control system (APs), the rapid warning of volatile gas of flammable substances was realized. Experiments showed that RAs has satisfied selectivity to volatiles of usual flammable liquid, such as the output voltage reaches 3 V (0 - 3.3 V). With simulation about the actual installation state in bus, MWs sounds an alarm at 2 minutes after splashing 50 mL 92# petrol to the floor. For the last two years, FEVMEW has been integrated into more than 4000 buses in Hefei. This design has been proved feasible according to the actual operation.

Application of Neural Networks in Early Warning Systems for Coronary Heart Disease

This paper presents a BP neural network-based algorithm for the iden-tification of coronary heart disease through the clinical data of cardiology for many years and the personal physiological attributes easily obtained in daily life.The goal of this paper is to judge whether it may have coronary heart disease by testing the attribute values of the tester.First,through the training of samples,the net-work model structure is designed,and a relatively good neural network model is obtained.Second,according to the model,the possibility of coronary heart disease was calculated.

Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring

Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.

Pattern changes and early risk warning of Spartina alterniflora invasion:a study of mangrove-dominated wetlands in northeastern Fujian,China

The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.

Integrated water risk early warning framework of the semi-arid transitional zone based on the water environmental carrying capacity (WECC)

Water risk early warning systems based on the water environmental carrying capacity(WECC)are powerful and effective tools to guarantee the sustainability of rivers.Existing work on the early warning of WECC has mainly concerned the comprehensive evaluation of the status quo and lacked a quantitative prejudgement and warning of future overload.In addition,existing quantitative methods for short-term early warning have rarely focused on the integrated change trends of the early warning indicators.Given the periodicity of the socioeconomic system,however,the water environmental system also follows a trend of cyclical fluctuations.Thus,it is meaningful to monitor and use this periodicity for the early warning of the WECC.In this study,we first adopted and improved the prosperity index method to develop an integrated water risk early warning framework.We also constructed a forecast model to qualitatively and quantitatively prejudge and warn about the development trends of the water environmental system.We selected the North Canal Basin(an essential connection among the Beijing-Tianjin-Hebei region)in China as a case study and predicted the WECC in 25 water environmental management units of the basin in 2018–2023.We found that the analysis of the prosperity index was helpful in predicting the WECC,to some extent.The result demonstrated that the early warning system provided reliable prediction(root mean square error of 0.0651 and mean absolute error of 0.1418),and the calculation results of the comprehensive early warning index(CEWI)conformed to the actual situation and related research in the river basin.From 2008 to 2023,the WECC of most water environmental management units in the basin had improved but with some spatial differences:the CEWI was generally poor in areas with many human disturbances,while it was relatively good in the upstream regions with higher forest and grass covers as well as in the downstream areas with larger water volume.Finally,through a sensitivity analysis of the indicators,we proposed specific management measures for the sustainability of the water environmental system in the North Canal Basin.Overall,the integrated water risk early warning framework could provide an appropriate method for the water environmental administration department to predict the WECC of the basin in the future.This framework could also assist in implementing corresponding management measures in advance,especially for the performance evaluation and the arrangement of key short-term tasks in the River Chief System in China.