Typhoon/Hurricane/Tropical Cyclone Disasters: Prediction, Prevention and Mitigation

Since 1972 Rita typhoon attacked on Dalian Port and induced severe catastrophe, we were studied on statistical prediction model of typhoon induced wave height and wind speed. With an increasing tendency of the natural hazards frequency and intensity, risk assessment of some design codes for coastal defence infrastructures should be of paramount importance influencing the economic development and a lot of lifes in China. Comparison between existing extreme statistical model like Gumbel, Weibull, P-III distribution or Probable Maximum Typhoon/Hurricane (PMT/PMH), Design Basis Flood (DBF) with our 1975-1980 proposed (CEVD) model showed that all the planned, designed and constructed coastal infrastructures accepted the traditional safety regulations are menaced by possibility of future ty-phoon/hurricane disasters and cannot satisfy the safety requirements with the increasing tendency of the extreme natural hazards. Our first publication in US (J. of Waterway Port Coastal & Ocean Eng. ASCE, 1980, ww4) proposed an new model “Compound Extreme Value Distribution” used for China sea, after then the model was used in “Long term Distribution of Hurricane Characteristics” for Gulf of Mexico & Atlantic coasts, U.S. (OTC.1982). 2005 hurricane Katrina, Rita and 2012 hurricane Sandy induced disasters proved 1982 CEVD and CEVD has been developed into Multivariate Compound Extreme Value Distribution (MCEVD). 2006 MCEVD predicted extreme hazards in New Orleans, Gulf of Mexico and Philadelphia areas. 2013 typhoon Fitow induced disaster in China also proved MCEVD 2006 predicted results.

Agrometeorological wheat yield prediction in rainfed Potohar region of Pakistan

The reliable early estimates of production had always been the prime concerns of growers on one hand and planners as well as policy makers for import/export on the other hand. This study represents a linear regression model making use of meteorological parameters at critical stages of crop’s life cycle to predict the wheat yield about two months earlier than the harvesting. A statistical based software “Statistical Package for Social Sciences” (SPSS) and MS-excel were employed as working tools. Decadal (ten days) agrometeorological data for Rabi season (for the period 1993-2011) being observed at Regional Agromet Centre, Rawalpindi have been utilized. The parameters studied for correlation were mainly rainfall (amount and days), air temperature (minimum, maximum, mean), heat units (on phenological basis), relative humidity, wind speed, sunshine duration, reference crop evapotran-spiration etc. Finally, minimum temperature, sunshine duration and rainfall amount in January (tillering and stem extension phase) proved to be the reliable predictors for the final yield. The correlation coefficients for these parameters on individual basis resulted within the acceptable range where as in aggregate it remained 0.87, an optimistic value.

Disaster prediction of coal mine gas based on data mining

The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.

Longwall face roof disaster prediction algorithm based on data model driving

Hydraulic support is the primary equipment used for surrounding rock control at fully mechanized mining faces.The load,location,and attitude of the hydraulic support are important sets of basis data to predict roof disasters.This paper summarized and analyzed the status of coal mine safety accidents and the primary influencing factors of roof disasters.This work also proposed monitoring characteristic parameters of roof disasters based on support posture-load changes,such as the support location and support posture.The data feature decomposition method of the additive model was used with the monitoring load data of the hydraulic support in the Yanghuopan coal mine to effectively extract the trend,cycle period,and residuals,which provided the period weighting characteristics of the longwall face.The autoregressive,long-short term memory,and support vector regression algorithms were used to model and analyze the monitoring data to realize single-point predictions.The seasonal autoregressive integrated moving average(SARIMA)and autoregressive integrated moving average(ARIMA)models were adopted to predict the support cycle load of the hydraulic support.The SARIMA model is shown to be better than the ARIMA model for load predictions in one support cycle,but the prediction effect of these two algorithms over a fracture cycle is poor.Therefore,we proposed a hydraulic support load prediction method based on multiple data cutting and a hydraulic support load template library.The constructed technical framework of the roof disaster intelligent prediction platform is based on this method to perform predictions and early warnings of roof disasters based on the load and posture monitoring information from the hydraulic support.

An Effective Prediction Method for Supporting Decision Making in Real Estate Area Selection

Real estate has been a dominant industry in many countries. One problem for real estate companies is determining the most valuable area before starting a new project. Previous studies on this issue mainly focused on market needs and economic prospects, ignoring the impact of natural disasters. We observe that natural disasters are important for real estate area selection because they will introduce considerable losses to real estate enterprises. Following this observation, we first develop a self-defined new indicator named Average Loss Ratio to predict the losses caused by natural disasters in an area. Then, we adopt the existing ARIMA model to predict the Average Loss Ratio of an area. After that, we propose to integrate the TOPSIS model and the Grey Prediction Model to rank the recommendation levels for candidate areas, thereby assisting real estate companies in their decision-making process. We conduct experiments on real datasets to validate our proposal, and the results suggest the effectiveness of the proposed method.

Yellow River Valley flood and drought disaster：spatial－temporal distribution prediction and early－warning

By means of analysing the historical data of flood-drought grade series in the past 2000 years(A.D.0-1900),especially in the last 5000 years (1470-1900) , this paper revealed the spatial-temporaldistribution features of severe flood and drought in Yellow River Valley. Statistical methods of varianceanalysis, probability transition and the principles of scale correspondence were employed tocomprehensively predicate 90's tendency of severe flood and drought in the Yellow River Valley. In addi-tion, this paper pointed out the possible breaching dikes, sectors and the flooding ranges by future's se-vere flood, meanwhile estimating the associated economic losses and impact to environment.

Intensifying Earthquake Prediction in the Light of Action of International Decade for Natural Disaster Reduction

This paper is a review on earthquake prediction and forecast research,progress in earthquake prediction work and pre-estimation of earthquake hazard degree in China in recent years.It indicates that China is the first country,the government of which has promoted and organized the state administration department on reduction of seismic hazards and ensured the socialization of earthquake prediction and forecast in the world.A program of earthquake prevention and protection and hazard reduction based on the results of research on earthquake occurrence regularities and prediction of earthquake preparation trend has been completed,and hence the socialization of results of earthquake prediction and forecast research can be expected to be in practice.The practical seismological works in last 20 years indicate that the earthquakes are not considered to be unpredictable,but it is a challenge remaining to be accepted.We are willing to cooperate with all friends who are engaged in earthquake prediction and

RS and GIS-based Statistical Analysis of Secondary Geological Disasters after the 2008 Wenchuan Earthquake

Using RS and GIS means, this article analyzes the general geological characteristics and the structural belt distribution features in Wenchuan County, Sichuan province, P.R. China as well as the characteristics of the large-scale landslides, mud-rock flows, earthquake lakes, etc., after the earthquake on May 12, 2008. Based on the above work, comprehensive indoor and outdoor research is launched on disaster distribution characteristics and their relationship with earthquakes, terrains, strata, lithology, and structures. Weights of evidence method is utilized to quantitatively analyze and evaluate the spatial distribution of secondary geological disasters after the earthquake occurred. 3 remedying grades for secondary geological disasters are derived from the results of the weights of Evidence, followed by suggestions given to remedy earthquake secondary disasters.

Support principles of NPR bolt/cable and control techniques of large-deformation disasters

Deep mining has been paid much more attention because of the depletion of shallow mining resources.Traditional bolts could be invalid to accommodate large displacement and deformation in geomaterials.Consequently, alternative support and reinforcement bolts need to be studied and their constitutive models also need to be developed to help understanding for the complex stress-strain responses of rock masses under loadings. The effect of Negative Poisson's Ratio(NPR) that is attributed to the swelling phenomenon along the lateral direction may appear in metal materials under tensional loadings. Thence NPR materials often have an advantage over NPR ones in mechanical behavior such as impact resistance, antishearing, and energy absorbed. From the characteristics of NPR materials, a series of bolt and cable supports with the effect of NPR and constant-resistance have been recently developed. We here firstly introduce the structural features of NPR support. Then the constitutive model of NPR support is presented and its corresponding equation of energy equilibrium. Its basic principle interacted on rock masses is further discussed. Finally, NPR cables are employed to support the slope of an open-pit mine. The applications show that NPR cables can ease failure within the slope and play an important role in predicting and providing early warning of slope failure, together with a monitoring system of slope stability.

Study of Simulation of Earth-Fissures Development and Prediction Based on GIS

Earth-Fissure in Linfen city is dominated by many factors. In this paper, every factor is analyzed in detail and each coverage of them is established. After that, reciprocal relationship between them is determined by using AHP method. With the strong spatial-operation fuuction or GIS, the advanced GIS models of earth-fissure simulation and multi-source forecast are built. On the basis of this, asatisfied prediction has provided extremely important science bases for the city future plans.

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world.Therefore,timely and accurate decision-making is essential for mitigating flood-related damages.The traditional flood prediction techniques often encounter challenges in accuracy,timeliness,complexity in handling dynamic flood patterns and leading to substandard flood management strategies.To address these challenges,there is a need for advanced machine learning models that can effectively analyze Internet of Things(IoT)-generated flood data and provide timely and accurate flood predictions.This paper proposes a novel approach-the Adaptive Momentum and Backpropagation(AM-BP)algorithm-for flood prediction and management in IoT networks.The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency.Real-world flood data is used for validation,demonstrating the superior performance of the AM-BP algorithm compared to traditional methods.In addition,multilayer high-end computing architecture(MLCA)is used to handle weather data such as rainfall,river water level,soil moisture,etc.The AM-BP’s real-time abilities enable proactive flood management,facilitating timely responses and effective disaster mitigation.Furthermore,the AM-BP algorithm can analyze large and complex datasets,integrating environmental and climatic factors for more accurate flood prediction.The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%,96.4%F1-Measure,97%Precision,and 95.9%Recall.The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks,contributing to more resilient and efficient flood control strategies,and ensuring the safety and well-being of communities at risk of flooding.

Research on prediction for coal and gas outburst based on Matlab neural network toolbox and its application

In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.

An efficient framework for ensemble of natural disaster simulations as a service

Calculations of risk from natural disasters may require ensembles of hundreds of thousands of simulations to accurately quantify the complex relationships between the outcome of a disaster and its contributing factors.Such large ensembles cannot typically be run on a single computer due to the limited computational resources available.Cloud Computing offers an attractive alternative,with an almost unlimited capacity for computation,storage,and network bandwidth.However,there are no clear mechanisms that define how to implement these complex natural disaster ensembles on the Cloud with minimal time and resources.As such,this paper proposes a system framework with two phases of cost optimization to run the ensembles as a service over Cloud.The cost is minimized through efficient distribution of the simulations among the cost-efficient instances and intelligent choice of the instances based on pricing models.We validate the proposed framework using real Cloud environment with real wildfire ensemble scenarios under different user requirements.The experimental results give an edge to the proposed system over the bag-of-task type execution on the Clouds with less cost and better flexibility.

Research on the Construction of Agricultural Meteorological Disaster Prevention Service System in Henan Province Under the Rural Revitalization Strategy

The rural revitalization strategy regards the construction of a modern meteorological service system as an important part of the implementation of a new round of high-standard farmland construction planning. Preventing agricultural meteorological disasters is a prerequisite to ensure that the total agricultural output of Henan Province remains stable and the transformation of Henan Province from a major agricultural province to a strong agricultural province. Therefore, it is necessary to study and construct the agricultural meteorological disaster prevention service system in Henan Province. Through research and documentation, based on the perspective of rural revitalization strategy, the status and problems of the construction of the agricultural meteorological prevention defense service system in Henan Province were studied, and countermeasures and suggestions for optimizing the agricultural meteorological disaster prevention service system in Henan Province were put forward, hoping to provide decision-making reference for agricultural development in Henan Province.

干旱形成机制与预测理论方法及其灾害风险特征研究进展与展望

在全球变暖背景下,干旱事件发生的频率和强度不断增大、影响不断加重,干旱发生规律的异常性和机制的复杂性也更为突出,对干旱形成机制、预测理论方法及灾害风险变化规律等方面都提出了新的挑战,也制约了当前干旱预测、预警及其灾害防控能力的提高。近年来,在国家重点基础研究发展计划(973计划)课题等多个国家级项目支持下,已在干旱灾害形成机制与预测理论方法及其风险特征方面取得了一系列新成果。通过动力诊断、数值模拟和田间试验等方法,开展了干旱形成的多因子协同作用和多尺度叠加机制、干旱致灾过程的逐阶递进特征,以及干旱灾害风险分布演化的主控因素等方面的研究。对如下几方面的新进展进行了系统总结归纳:(1)厘清了全球变暖背景下青藏高原热力、海温、夏季风、遥相关等多因子对干旱形成的作用机制。(2)发现了降水亏缺时间尺度和农作物不同生长阶段的干旱敏感性规律。(3)揭示了变暖背景下典型区域干旱灾害风险分布及其变异的新特征;构建了干旱灾害风险新概念模型。(4)研发了东亚季风区的季节和次季节干旱集成预测系统。在总结归纳已取得研究成果的基础上,对未来干旱形成机制及其灾害风险科学研究进行了展望,提出了5个重点研究方向:(1)多因子联动及其多尺度叠加效应对干旱形成的影响;(2)系统整合人类活动和决策以及相关反馈的气候模式研究;(3)揭示陆-气耦合和大气环流协同作用对干旱的影响;(4)认识干旱灾害对粮食安全和生态安全影响的关键过程;(5)提高不同气候情景下干旱预估的准确度。

New Seismic Zoning Map of Chin a and Its Application in Earthquake Disaster Mitigation

The New Seismic Zoning Map of China was prepared from 1987 to 1990 and officially promulgated in 1991.In comparison with the previous two seismic zoning maps prepared in 1957 and 1977,some new methods were applied to upgrade the method currently used for seismic hazard analysis.First,a probabilistic method was used instead of the deterministic analysis was used for previous mapping.Second,by taking advantages of the long history of historical seismic data in China,the nonhomogeneity of seismicity both in space and in time has been fully considered and hence the over-and/or underestimation of seismic hazard could be avoided.Third,the results of middle-and long-term earthquake prediction based on tectonic evidence have been incorporated into seismic hazard analysis.In addition,the attenuation laws for both intensity and peak acceleration of strong motion as the mapping parameters are also presented.Finally,an evaluation of the New Seismic Map and its effect on engineering application,such as aseismic

REGIONAL DISTRIBUTION OF MAIN AGROMETEOROLOGICAL DISASTERS AND THEIR MITIGATION COUNTERMEASURES IN CHINA

It is described in this paper the regional distribution of main meteorological disasters and their important effects on agricultural production of China, and summarizes the controlling system of agrometeorological disasters and its basic principles and the research achievements in this field.

基于RBF网络的新疆特重雪灾区最大积雪深度预测研究

基于建立的雪灾灾损指数,确定新疆特重雪灾区域;进一步聚焦特重雪灾区的8个县(市),包括阿勒泰市、福海县、青河县、塔城市、托里县、沙湾市、尼勒克县和伊宁县,分别建立县域RBF网络模型,预测2021—2050年年最大积雪深度。结果表明:该模型可用于新疆特重雪灾区最大积雪深度预测,但预测精度仍有待提升;塔城市、尼勒克县将于2025—2029年连续出现最大积雪深度偏高事件,2039年青河县将出现最大积雪深度的极大值,因此应关注可能发生雪灾的年份与县(市),积极做好雪灾的防御工作。

冬小麦农业气象灾害风险预警的业务实践与思考

开展冬小麦农业气象灾害风险预警工作是当前保障小麦生产安全和防灾减损的重要工作之一。2022年开始气象部门和农业部门联合开展了冬小麦农业气象灾害风险预警的业务服务工作,取得了一些技术成果和成功案例,但也存在一些问题和不足。本文简述了农业气象灾害风险预警业务的内涵,提出了综合农业气象灾害可能性预报、危险性和脆弱性预估,构建基于风险矩阵的冬小麦农业气象灾害风险预警业务的技术框架,并以2022年小麦干热风风险预警工作为例,展示了冬小麦农业气象灾害风险预警工作的技术流程。冬小麦农业气象灾害风险预警工作刚刚起步,尚有很多工作不完善,包括如何与已有的灾害风险评估成果进行深入融合,开展定量化的灾害影响预报和预警指标率定等。此外,在提升冬小麦农业气象灾害风险预警的准确率和开展服务效益评估等方面,未来还需要做大量的研发工作。