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.

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.

Early warning model for slope debris flow initiation

Early warning model of debris flow is important for providing local residents with reliable and accurate warning information to escape from debris flow hazards. This research studied the debris flow initiation in the Yindongzi gully in Dujiangyan City, Sichuan province, China with scaled-down model experiments. We set rainfall intensity and slope angle as dominating parameters and carried out 20 scaled-down model tests under artificial rainfall conditions. The experiments set four slope angles(32°, 34°, 37°, 42°) and five rainfall intensities(60 mm/h, 90 mm/h, 120 mm/h, 150 mm/h, and 180 mm/h) treatments. The characteristic variables in the experiments, such as, rainfall duration, pore water pressure, moisture content, surface inclination, and volume were monitored. The experimental results revealed the failure mode of loose slope material and the process of slope debris flow initiation, as well as the relationship between the surface deformation and the physical parameters of experimental model. A traditional rainfall intensity-duration early warning model(I-D model) was firstly established by using a mathematical regression analysis, and it was then improved into ISD model and ISM model(Here, I is rainfall Intensity, S is Slope angle, D is rainfall Duration, and M is Moisture content). The warning model can provide reliable early warning of slope debris flow initiation.

A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network

Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.

Remote Monitoring and Early Warning Model of Frozen Soil in Dam Areas

In view of the cumbersome and often untimely process of manual collection and observation of frozen soil data parameters,and the damage caused to dams by frost heaving of frozen soil,a remote monitoring and an early warning model for frozen soil in dam areas was presented.The Pt100 temperature sensors and JM seam gauges were used as measurement tools in the system.The sensor layout was designed,based on the actual situation in the monitoring area.A 4G network was used for wireless transmission to monitor frozen soil data in real time.BP neural network was used to predict the parameters of frozen soil.After analysis,four factors including the average temperature of frozen soil,the type of frozen soil,the artificial upper limit of frozen soil and the building construction time were selected to establish an early warning model using fuzzy reasoning.The experimental results showed that the early warning model could reflect the influence on dam buildings of frost heaving and sinking of frozen soil,and provided technical support for predicting the hazard level.

Application of Incident Chain Model and Targeted Dissemination Technology in Early Warning System

In order to improve the accuracy and efficiency of early warning system, the incident chain model and the targeted dissemination technology are proposed in this paper. Firstly, the occurrence probability, affected area and duration of disaster are predicted with the incident chain model and GIS. According to prediction results, the early warning system can accurately deliver early warning information specifically to the affected areas through targeted dissemination. Moreover, dissemination performance can also be evaluated in real time after early warning information dissemination, so that everyone in the affected area can receive early warning information successfully. The incident chain model and the targeted dissemination technology presented in this study are of great significance for improving the information dissemination ability of early warning system.

Financial crisis early-warning model of listed companies based on predicted value

To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.

批发零售业上市公司财务困境预警--基于RF-VNWOA-LSSVM模型

本文从国泰安数据库(CSMAR)选取2019—2022年度A股主板被ST或被*ST的33家批发零售业上市公司作为研究样本,选取20个财务指标和9个非财务指标,构建了预警指标体系。为消除非关键特征指标的影响,采用随机森林算法(RF)进行特征值筛选,将筛选的数据集应用于经过优化的LSSVM(最小二乘支持向量机)进行财务预测和预警。实验结果显示,相较于传统的PSO(粒子群优化算法)、GA(遗传算法)以及WOA(鲸鱼优化算法),采用VNWOA优化算法的分类精度分别提高了2.9个百分点、2.9个百分点以及4.35个百分点。综合应用了随机森林和VNWOA优化算法的RF-VNWOA-LSSVM模型在分类精度上相较于RF-费希尔判别法和BP神经网络分别提高了18.75个百分点、8.45个百分点。实验结果表明本文提出的RF-VNWOALSSVM预警模型可以对财务风险进行有效识别。

Early-warning signals for an outbreak of the influenza pandemic

Over the course of human history, influenza pandemics have been seen as major disasters, so studies on the influenza virus have become an important issue for many experts and scholars. Comprehensive research has been performed over the years on the biological properties, chemical characteristics, external environmental factors and other aspects of the virus, and some results have been achieved. Based on the chaos game representation walk model, this paper uses the time series analysis method to study the DNA sequences of the influenza virus from 1913 to 2010, and works out the early-warning signals indicator value for the outbreak of an influenza pandemic. The variances in the CCR wall〈 sequences for the pandemic years （or ＋ -1 to 2 years） are significantly higher than those for the adjacent years, while those in the non-pandemic years are usually smaller. In this way we can provide an influenza early-warning mechanism so that people can take precautions and be well prepared prior to a pandemic.

Early Warning of Foundation Settlement Deformation for Ballastless High-Speed Railway Tracks

There has been rapid development of high-speed railway lines, especially passenger-dedicated railway lines, in China. Trains are traveling at speeds exceeding 250 km per hour and they require highly smooth tracks to ensure safety. However, there have been no in-depth studies on the early warning of the settlement of high-speed railway lines in China or abroad. Most methods use a simple model based on data processing and decision rules. The core issues of early warning lie in the science and rationality of decision rules. The present paper therefore investigates novel and critical indexes for the warning of settlement under high-speed railway lines according to existing norms and field data, and several essential indexes of deformation warning are suggested through theoretical and experimental analysis.

Introduction to marine emergency forecasting and early-warning system(MEFES)

Marine emergencies especially oil spill may bring irreversible harm to the marine environment,and will cause immeasurable economic losses.In recent years,the demand for crude oil is increasing year by year in China with the high-speed economic development,leading to the high risk of marine oil spill.Therefore,it is necessary that promoting emergency response on marine oil spill in China and improving oil spill forecasting and early-warning techniques.This paper introduces the Marine Emergency Forecasting and Early-warning System(MEFES)developed by National Marine Data and Information Service(NMDIS).The system consists of one database,two modelling subsystems and a GIS platform.The database is the marine emergency database,and two subsystems include the marine environmental forecasting subsystem and the oil spill behaviour forecasting subsystem.MEFES has been applied in the emergency response of some major oil spill accidents occurred in recent years.The operational applications of the system can provide some theoretical basis and reference for marine oil spill emergency response.

云南省地质灾害气象风险预警技术改进及应用检验

利用2014—2022年云南省地质灾害数据和气象降水资料,开展有效降水量致灾概率研究,并结合地质灾害易发因子,分区建立了新一代精细化地质灾害气象风险预警模型,并对模型性能开展了应用检验。结果表明:新建的地质灾害气象风险预警模型提高了预警产品的时空分辨率和准确率,应用效果良好。模型能提前较好地反映出区域性强降水过程背景下的气象风险预警等级;对较难预报的局地强降水,也可以通过实时更新的监测预警产品给出风险加强的趋势,提前发出地质灾害气象风险预警信息;能直接客观反映出极端强降水背景下的气象风险高级别预警,起到较好的预警提示作用。

基于风险评估模型的传染病预警体系的建立

目的建立一套基于风险评估模型的传染病预警体系,推动传染病管理水平的提升。方法通过搜集、整理最新的研究成果,参考相关的传染病预警评价指标体系的理论框架,构建基于风险评估模型的传染病预警体系。结果本次研究共纳入了12名专家,筛选出了三个一级指标,涉及流行前期指标、典型症状期指标、非典型症状期指标等,同时涉及7个二级指标。体系的必要性平均得分为8.27±0.24分,可获得性平均得分为7.74±0.34分。结论基于风险评估模型的传染病预警体系的建立有利于当地及时掌握传染病流行趋势,并进行调查指标分布趋势分析,然后提出相应的预警方案,可为有效预防传染病提供相关的依据。

复杂多方向威胁下的导弹预警雷达优化部署方法

针对现有导弹预警雷达部署相对独立、协同困难,难以满足大规模对抗场景的现状,从远程预警雷达、跟踪识别雷达、机动式预警雷达不同的任务特点出发,建立应对复杂多方向威胁的多型导弹预警雷达优化部署模型,在满足最优覆盖、协同交接、目标识别等任务约束下,解决雷达协同部署问题。针对所提模型设计了一种基于云自适应的分区优化离散粒子群(CPBPSO)算法,通过设计分区编码策略缩减算法求解空间、加入云自适应变异算子提高算法全局寻优和局部跳出能力,使算法更适用于导弹预警雷达部署问题的处理。实例验证了所提模型在求解单方向、多方向威胁场景部署问题的可行性,对比分析了CPBPSO算法的有效性,基本满足导弹预警雷达最优化协同部署的需求。

基于大数据的高校学生心理危机智能预警模型构建

目的本研究基于大学生心理健康测评数据,综合学生基本信息及日常行为数据,利用大数据、人工智能技术,探索建立大学生心理危机智能预警模型。方法采用整群抽样法,选取某高校部分在校大学生作为测试样本;采取大数据技术,分析提取影响大学生心理健康问题的特征量;采取神经网络技术,构建大学生心理危机预警模型。结果1)成功提取大学生心理健康问题的有效特征量,其中心理健康症状特征因子4个,分别是强迫、人际关系敏感、抑郁等3个单症状因子,1个多症状因子;学生基本信息特征量4个,分别是母亲教养方式、父亲教养方式、家庭经济条件、有无心理治疗(咨询)史;学生日常行为特征量2个,分别是学生学业情况和出勤情况。2)实现特征量归一化处理,通过数据对比分析及特征量的影响大小,分别对3个方面的10个特征量赋予权重并进行归一化处理。3)完成大学生心理危机预警模型构建。结论本研究提取了影响大学生心理健康问题的主要特征,建立了大学生心理危机预警指标体系,并结合心理危机预警等级,利用神经网络技术,搭建了大学生心理危机预警模型,为及时有效干预大学生心理危机提供了新的解决方案。

基于Deep Forest算法的对虾急性肝胰腺坏死病(AHPND)预警数学模型构建

为预报池塘养殖凡纳对虾(Penaeus vannamei)急性肝胰腺坏死病(AHPND)的发生,自2020年开始,笔者对凡纳对虾养殖区开展了连续监测工作,包括与疾病发生相关的环境理化因子、微生物因子、虾体自身健康状况等18个候选预警因子指标,通过数据标准化处理后分析病原、宿主与环境之间的相关性,对候选预警因子进行筛选,基于Python语言编程结合Deep Forest、Light GBM、XGBoost算法进行数据建模和预测性能评判,仿真环境为Python2.7,以预警因子指标作为输入样本(即警兆),以对虾是否发病指标作为输出结果(即警情),根据输入样本和输出结果各自建立输入数据矩阵和目标数据矩阵,利用原始数据矩阵对输入样本进行初始化,结合函数方程进行拟合,拟合的源代码能利用已知环境、病原及对虾免疫指标数据对目标警情进行预测。最终建立了基于Deep Forest算法的虾体(肝胰腺内)细菌总数、虾体弧菌(Vibrio)占比、水体细菌总数和盐度的4维向量预警预报模型,准确率达89.00%。本研究将人工智能算法应用到对虾AHPND发生的预测预报,相关研究结果为对虾AHPND疾病预警预报建立了预警数学模型,并为对虾健康养殖和疾病防控提供了技术支撑和有力保障。

基于注意力机制的CNN-LSTM-XGBoost台风暴雨电力气象混合预测模型

极端台风暴雨灾害具有非线性、极差大以及多峰值等特点。为使电网及时获取预警信息,提出一种基于注意力机制的CNN-LSTM-XGBoost台风暴雨电力气象混合预测模型。首先,利用基于注意力机制的卷积神经网络(CNN)辨识关键台风暴雨灾害特征;然后,利用长短期记忆网络(LSTM)训练时间序列预测模型以挖掘台风暴雨时序特征,使用极限梯度提升算法替换模型输出层以缓解过拟合问题;最后,以2023年台风泰利为例验证所提方法的有效性。算例分析表明,所提模型具有较高的准确性,对预测精度的提升可达40.84%以上。

金融危机预警情报的失灵与治理

[研究目的]金融安全是国家安全的重要构成部分。金融危机预警作为金融监管的前哨和第一道防线,可将危机扼杀在摇篮中或将灾害损失降到最少。因此,探究金融危机预警情报服务框架,不仅为完善金融风险治理提供新的思路,还有助于提升情报服务金融安全的能力和水平。[研究方法]基于情报学视角,采用访谈和文献调查方法,对金融危机预警情报失灵的根源进行探析,构建了金融危机预警情报框架,并从预警原则、情报感知、分析策略和预警机制等方面进行阐释。[研究结论]研究表明,实施分级分解的分析战略、转变预警方式和强化危机预警机制等手段,有助于在不确定风险中提高危机管理响应效能,减少危机预警情报失灵的发生。

中国锂资源安全评估与预警

锂是中国重要的战略性矿产资源,在锂资源需求快速增加、进口依赖严重的背景下,对中国锂资源安全进行评估与预警具有必要性和紧迫性。构建了以资源禀赋、进口安全、市场风险和地缘政治为一级指标的中国锂资源安全评估指标体系,利用常权和变权模型对2009—2021年中国锂资源安全状况进行了评估,采用ARIMA-BP组合模型对2022—2035年中国锂资源安全进行了预警。研究发现,①进口依赖度、进口集中度和运输通道风险对锂资源安全的影响最大;②2009—2021年中国锂资源安全等级由黄色—基本安全等级波动下降至橙色—不安全等级,资源禀赋和进口安全是导致锂资源安全等级较低的主要原因;③2022—2035年中国锂资源安全评分总体呈下降趋势,除2026年处于黄色—基本安全等级外,其余年份均处于橙色—不安全等级。

基于文本数据的灾害信息风险监测与预警集成方法研究

基于灾害信息的文本数据,该文提出一种风险监测与预警集成方法,以提高灾害信息风险监测效率及监测支持预警的针对性。首先,运用八爪鱼数据采集器采集和处理灾害信息的文本数据;其次,运用BTM主题模型构建灾害信息风险监测模型,得到主题热度趋势并确定具有高风险且亟待预警的话题;然后,提出灾害信息风险预警指标体系和预警等级判定方法,计算所确定话题的风险综合指标预警加权值,并结合预警区间判定预警等级;最后,以2021年“7·20”郑州特大暴雨灾害信息的风险监测和预警为例进行实验分析,验证所提方法的可行性。研究表明,该方法可将灾害信息的文本数据贯穿于风险监测与预警两个过程,有助于为监测结果更有针对性地支持预警工作提供参考。