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.

The Application of China's High Speed Railway Earthquake Early Warning System in Jakarta-Bandung HSR

Earthquake has a significant impact on operation safety of the high speed railway,and for Jakarta-Bandung High Speed Railway(HSR)in Indonesia where it is earthquake-prone,it is necessary to establish an earthquake early warning system to strengthen its earthquake resistance.Based on the principle and technical characteristics of China's high speed railway earthquake early warning system and combining the actual situations of Jakarta-Bandung HSR in Indonesia,this paper describes how to implement China's high speed railway earthquake early warning system in Jakarta-Bandung HSR.It focuses on optimizations in environmental adaptation design and seismic network interface design,earthquake attenuation model parameter adjustment and terminal software interface adjustment,so as to make the system better suit the local situations,and meet operation requirements and guarantee safe operation of Jakarta-Bandung HSR.

Street Lamp Status Warning System Based on Internet of Things Technology

This paper proposes a street light warning system based on Internet of Things(IoT)technology,which uses cameras to detect moving targets such as vehicles and pedestrians around the system and adjust the brightness of street lights according to road conditions to reduce unnecessary power waste.The system has a mature self-fault detection mechanism and is equipped with a wireless communication device for data exchange and timely communication with the host computer terminal.The intelligent street lamp system in this paper can be used to reduce the occurrence of pedestrian and vehicle accidents at intersections,and at the same time reduce the consumption of manpower and material resources for street lamp troubleshooting,to achieve energy conservation and emission reduction.

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.

Laws of strata energy release and corresponding safety warning system in metal mine

The strata deformation in mining area was monitored in Dabaoshan copper-iron mine,and an analytical method of strata energy release was put forward.On the basis of chaotic theory,by reconstructing the phase space for time series data of strata energy release,the saturated embedding dimension and the correlation dimension of the dynamic system were obtained to be 4 and 1.212 8,respectively,and the evolution laws of distances between phase points of strata energy release in the phase space were revealed.With grey theory,a prediction model of strata energy release was set up,the maximum error of which was less than 6.7%.The results show that there are chaotic characters in strata energy release during mining;after reconstructing phase space,the subtle changing characteristics of energy release can be magnified,and the internal rules can be fully demonstrated.According to the laws,a warning system for strata stability in mining area was established to provide a technical safeguard for safe mining.

Preliminary Study on Early-warning System of Ecological Security in Water-Level-Fluctuating Zone (WLFZ) of Three Gorges Reservoir

The area,the scope as well as some ecological environment questions in Three Gorges Reservoir was briefly introduced. Then its early warning-system frame was preliminarily constructed,which includes ecological security dynamic monitoring,ecological security appraisal,ecological security forecast and ecological security decision-making management. The synthetic evaluation indicator system of the ecological security quality were initially established,which includes ecological environment pollution,land use and land cover change,geological hazard and epidemic outbreaks. At the same time,29 evaluating indicators were selected,divides into the basic factors,response factors and inducing factors,which need to be Real-time monitored.

WebGIS-based Early Warning System of Farmland Environmental Pollution Caused by Livestock Waste

[Objective]The paper was to quickly get the real-time dynamic status of regional farmland environmental pollution caused by livestock wastes.[Method] With WebGIS as spatial information platform,the network and digital early warning system of farmland environmental pollution caused by livestock wastes was established.[Result] The system realized the functions such as livestock wastes calculation,livestock information query and analysis,nitrogen load quantity estimation of livestock waste,early warning of farmland environmental pollution caused by livestock wastes and visual display of result.[Conclusion] The paper provided scientific basis for the relevant research on farmland environmental pollution caused by livestock wastes.

Construction of Cucumber Powdery Mildew Early Warning System in Solar Greenhouse Based on Internet of Things

ln order to explore the design and construction of cucumber powdery mildew warning system in solar greenhouse, internet of things technology was used to conduct the real-time dynamic monitoring of the incidence of cucumber powdery mildew and cucumber growth environment in solar greenhouse. The growth environ-ment included temperature and humidity of air and soil. Logistic regression model was used to construct cucumber powdery mildew warning model. The results showed that humidity characteristic variable （maximum air humidity） and temperature characteristic variable （maximum air temperature） had significant effects on the inci-dence probability of cucumber powdery mildew in solar greenhouse. And it was fea-sible to construct cucumber powdery mildew warning system in solar greenhouse with internet of things.

A Real-time Monitoring and Early Warning System for Landslides in Southwest China

Landslides not only cause property losses,but also kill and injure large numbers of people every year in the mountainous areas. These losses and casualties may be avoided to some extent by early warning systems for landslides. In this paper, a realtime monitoring network and a computer-aided automatic early warning system(EWS) are presented with details of their design and an example of application in the Longjingwan landslide, Kaiyang County, Guizhou Province. Then, according to principle simple method of landslide prediction, the setting of alarm levels and the design of appropriate counter-measures are presented. A four-level early warning system(Zero, Outlook, Attention and Warning) has been adopted, and the velocity threshold was selected as the main warning threshold for the landslide occurrence, but expert judgment is included in the EWS to avoid false alarms. A case study shows the applicability and reliability for landslide risk management, and recommendations are presented for other similar projects.

Development of a monitoring and warning system based on optical fiber sensing technology for masonry retaining walls and trees

Hong Kong has a long history of applying masonry retaining walls to provide horizontal platforms and stabilize man-made slopes.Due to the sub-tropical climate,some masonry retaining walls are colonized by trees.Extreme weather,such as typhoons and heavy rains,may cause rupture or root failure of those trees,thus resulting in instability of the retaining walls.A monitoring and warning system for the movement of masonry retaining walls and sway of trees has been designed with the application of fiber Bragg grating(FBG)sensing technology.The monitoring system is also equipped with a solar power system and 4G data transmission devices.The key functions of the proposed monitoring system include remote sensing and data access,early warning,and real-time data visualization.The setups and working principles of the monitoring systems and related transducers are introduced.The feasibility,accuracy,serviceability and reliability of this monitoring system have been checked by in-site calibration tests and four-month monitoring.Besides,a two-level interface has been developed for data visualization.The monitoring results show that the monitored masonry retaining wall had a reversible movement up to 2.5 mm during the monitoring period.Besides,it is found that the locations of the maximum strain on trees depend on the crown spread of trees.

Outlining a stepwise,multi-parameter debris flow monitoring and warning system:an example of application in Aizi Valley,China

In recent years, the increasing frequency of debris flow demands enhanced effectiveness and efficiency of warning systems. Effective warning systems are essential not only from an economic point of view but are also considered as a frontline approach to alleviate hazards. Currently, the key issues are the imbalance between the limited lifespan of equipment, the relatively long period between the recurrences of such hazards, and the wide range of critical rainfall that trigger these disasters. This paper attempts to provide a stepwise multi-parameter debris flow warning system after taking into account the shortcomings observed in other warning systems. The whole system is divided into five stages. Differentwarning levels can be issued based on the critical rainfall thresholds. Monitoring starts when early warning is issued and it continues with debris flow near warning, triggering warning, movement warning and hazard warning stages. For early warning, historical archives of earthquake and drought are used to choose a debris flow-susceptible site for further monitoring. Secondly, weather forecasts provide an alert of possible near warning. Hazardous precipitation, model calculation and debris flow initiation tests, pore pressure sensors and water content sensors are combined to check the critical rainfall and to publically announce a triggering warning. In the final two stages, equipment such as rainfall gauges, flow stage sensors, vibration sensors, low sound sensors and infrasound meters are used to assess movement processes and issue hazardwarnings. In addition to these warnings, communitybased knowledge and information is also obtained and discussed in detail. The proposed stepwise, multiparameter debris flow monitoring and warning system has been applied in Aizi valley China which continuously monitors the debris flow activities.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Early Warning System of Important Meteorological Disasters in Hunan Province

Based on technologies of GRADS, Delphi, WEB and an SQLServer, distri- bution system for major meteorological disasters early warning information in Hunan Province was established. The system consists of warning tips, early warning for different media and early warning query statistics and the network terminal of the system platform includes a business database server, a message platform database server, a sending server and a receiving terminal. The system enjoys some func- tions, such as examination by different users, on-time updating and effectiveness of flood prevention, construction of excessive warning channel, construction of compre-hensive warning information platform and further improvement of information distribution. The system provides references for prevention and reduction of important me- teorological disasters in Hunan.

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.

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.

Design and Realization of Cold Disaster Early Warning System for Subtropics Aquaculture

By analyzing the subtropics aquaculture present situation,the necessity of the construction of cold disaster early warning system for subtropics aquaculture,the research goal and the duty were expounded. The system structure and the frame were introduced in detail. Several key questions and their solutions of the cold disaster early warning system for subtropics aquaculture were put forward.

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.