Early Warning Information Research Articles

Predicting 3-DoF motions of a moored barge by machine learning

The real-time prediction of a floating platform or a vessel is essential for motion-sensitive maritime activities. It can enhance the performance of motion compensation system and provide useful early-warning information. In this paper, we apply a machine learning technique to predict the surge, heave, and pitch motions of a moored rectangular barge excited by an irregular wave, which is purely based on the motion data. The dataset came from a model test performed in the deep-water ocean basin, at Shanghai Jiao Tong University, China. Using the trained machine learning model, the predictions of 3-DoF (degrees of freedom) motions can extend two to four wave cycles into the future with good accuracy. It shows great potential for applying the machine learning technique to forecast the motions of offshore platforms or vessels.

Monitoring critically saturated conditions for shallow landslide occurrence using electrical resistivity tomography

AbstractSoil wetness is an important property in determining the variable disposition of hillslopes to shallow landslides. Recent studies have demonstrated the potential of in situ soil wetness information for landslide early warning. However, the spatial representativeness of in situ sensors may be affected by local heterogeneities of soil properties and hydrological processes, and their installation may be destructive. Electrical resistivity tomography (ERT) has been used in the past to estimate plot‐scale soil moisture variation and may overcome these limitations. In this study, we installed and operated an automated ERT monitoring system on a landslide‐prone hillslope in the Napf region (Switzerland). The system was operational during a period of 9 mo, and measurements were conducted at high temporal resolution and under different soil hydrological conditions. Electrical resistivity was measured along two perpendicular profile lines in Wenner–Schlumberger configuration at 0.25‐m electrode spacing. Soil saturation was calculated by the Archie's law and the parameters were fitted with colocated soil moisture sensors. Comparison of ERT‐derived soil moisture with soil wetness from in situ sensors showed a good correlation, and infiltration properties critical for landslide early warning could be reliably reproduced. Further, analysis of spatial saturation variation revealed that ERT was capable to detect heterogeneities of soil hydrological process. Under highly saturated conditions, the reliability of the saturation estimation was affected by an increased number of faulty measurements and the spatial heterogeneity of the infiltration process.

Novel H7N7 avian influenza viruses detected in migratory wild birds in eastern China between 2018 and 2020

Wild birds are the natural reservoirs of avian influenza viruses, and surveillance and assessment of these viruses in wild birds provide valuable information for early warning and control of animal diseases. In this study, we isolated 19 H7N7 avian influenza viruses from wild bird between 2018 and 2020. Full genomic analysis revealed that these viruses bear a single basic amino acid in the cleavage site of their hemagglutinin gene, and formed four different genotypes by actively reassorting other avian influenza viruses circulating in wild birds and ducks. The H7N7 viruses bound to both avian-type and human-type receptors, although their affinity for human-type receptors was markedly lower than that for avian-type receptors. Moreover, we found that the H7N7 viruses could replicate efficiently in the upper respiratory tract and caecum of domestic ducks, and that the H5/H7 inactivated vaccine used in poultry in China provided complete protection against H7N7 wild bird virus challenge in ducks. Our findings demonstrate that wild bird H7N7 viruses pose a substantial threat to the poultry industry across the East Asian-Australian migratory flyway, emphasize the importance of influenza virus surveillance in both wild and domestic birds, and support the development of active control strategies against H7N7 virus.

Spatial Conflict Risk Measurement and Early Warning Based on Network Information Processing

The contradiction of land use is becoming more and more fierce in China, which poses a serious threat to the structural integrity and quantity of ecological land, the uncontrolled expansion of urban construction land, the imbalance between the spatial structure of agricultural land and ecological land, and the unreasonable spatial structure of various types of land within the city. This study mainly discusses the risk measurement and early warning of spatial conflict based on network information processing. In this study, GIS technology is used to deal with the risk of spatial conflict. Spatial conflict, in essence, is an objective geographical phenomenon arising from the competition for spatial resources in the process of human-land relations and is the result of the interaction between natural geographical factors and human factors, and natural geographical processes and human processes, and is a key factor affecting the sustainable development of the region. The logical network attack path composed of IP sequence captured by the space conflict network early warning and emergency response system is transformed into the attack path composed of a corresponding physical address sequence, which is directly displayed on the electronic map. At the same time, the attributes related to the spatial conflict events in the research area are also displayed in the form of a list. When the macro network early warning and emergency response system detect the occurrence of space conflict events in the study area, its emergency response subsystem queries the strategy database and outputs the emergency strategy in real-time by inputting the warning information analyzed by the early warning information analysis subsystem. Finally, the system realizes the functions of network node information visualization processing, network early warning and emergency response information visualization processing, and electronic layer basic operation. It is found that the industrial added value of the study area of ABC urban agglomeration has reached 283.140 billion yuan in recent three years, accounting for 60.25% of the industrial added value of the three cities and 30.98% of the whole province. The results show that the improper spatial regional combination has an important impact on the spatial conflict, and the improper spatial regional combination will significantly improve the level of spatial conflict.

Research on targeted service technology for early warning information of meteorological disaster based on NLP

Affected by global climate change and the superposition of urban construction, the climate of city meteorological guarantee service puts forward new requirements and new challenges. As the first line of defense of meteorological disaster prevention and reduction, it is very important to realize the refinement targeted service of early warning information. There are some problems in the traditional early warning information service, such as the difficulty of accurate service of early warning information, the lack of precision, and the insufficient mining of early warning text information. This paper mainly analyzes the text description characteristics of early warning information of meteorological disaster, constructs the early warning information knowledge extraction process, constructs the early warning information labeling system, and realizes the early warning effective time extraction method based on conditional random field model, Early warning affected areas extraction method based on bidirectional long-term and short-term memory neural network and early warning cautions extraction method based on bootstrapping weak supervised learning method. Finally, taking the early warning information targeting service of meteorological information decision support system as an example, this paper tests the early warning information extraction methods, and preliminarily realizes the early warning precision targeting service in the decision support service of meteorological disaster prevention and reduction.

Chinese Local Governments’ Behavioral Logic of Early-Warning Information Release Regarding Major Epidemic Outbreaks from the Organizational Analysis Perspective

Based on the three-dimensional ‘organizational goal – organizational field – organizational incen­tive’ analysis framework, this paper attempts to conduct a comparative and diachronic analysis of the behavioral logic of Chinese local governments’ early-warning information release in major epidemic outbreaks. The results of the research show that the organizational goal, field and incentive are highly rel­evant to different strategic choices of whether local governments should, will and are willing to perform their governance function; additionally, different combinations of these elements trigger different lo­cal government behaviors. The organizational goal, field and incentive are the starting point, turning point, and end point of local governments’ behavior­al logic, respectively, while the organizational field is prerequisite for the organizational incentive to work. The organizational goal – organizational field – organizational incentive sequence reflects the se­quence and interactive relationship of local govern­ments’ behavioral logic.

Flood Impacts Analysis and Mitigation Approach towards Community Resiliency at Nageshwari Upazila, Kurigram

Nageshwari is an upazila of Kurigram locality of Bangladesh. It is one of the most vulnerable areas to flood disasters. Every year many devastating floods occurred in this area. One of the main purposes of the study is to identify flood hazards and their impacts on the people's perception of the study area. Another purpose is to recognize mitigation approaches (both scientific and indigenous practices). To complete the aim, semi-structured questionnaires are used. Besides some key informant interviews and focus groups, discussions are also used to validate the collected data. The key findings of the study are the larger impact of inundation on health, agriculture, fisheries, road and transport, water and hygiene, and housing. From the overall impacts in the study area, the most affected sector is agriculture and roads. From people’s perception, many problems are found due to devastating floods. Unemployment, food scarcity, communication problems, etc. are the common problems in the study area. People practice some indigenous knowledge to reduce the flood impacts. Scientific practices are also applied by the authority to mitigate the flood impacts. But these mitigation measures are not enough toward accomplishes the demand. Some appropriate measures must be needed to decrease the flood impacts. Early warning systems and information dissemination on floods are not well developed. Sufficient and frequent training is needed about proper flood management, early warning, and dissemination within the lessons area. People take some adaptation techniques to diminish the flood. The local Government also takes some initiatives to decrease the flood impacts. But proper flood management needs to be improved to decrease the flood impacts. Proper management and awareness-raising programs will reduce the impact of inundation to enhance the community resiliency of the area.

Analysis and Management of Flu Disease Public Opinion Based on Machine Learning

In the new media era, there are more ways of information dissemination, and the speed of information dissemination becomes faster. Along with it, various public opinions and rumors flood the cyberspace. As a mainstream social media information publishing platform, microblog has become the main way for netizens to obtain, disseminate and publish information. Because microblog can freely make speeches, and has a fast transmission speed and a wide range, it is easy for public opinion information to be widely disseminated in a short time. In particular, information such as rumors in public opinion can affect the network environment and social stability. Therefore, it is necessary to analyze and predict public opinion changes and to provide early warning. The literature uses the classic BP-NN (BP-NN) as the base prediction model, and uses the information published on the Sina microblog platform as a sample to analyze and predict the public opinion of influenza diseases. Due to the BP-NN’ slow convergence speed, this paper introduces an improved genetic algorithm to select the optimal parameters in the BP-NN (IGA-BP-NN), shorten the calculation time, and improve the analysis and prediction efficiency. The experiments verify that the work in this paper can provide more accurate early-warning information for the public opinion management of related departments.

English

Weather and climate issues have become increasingly recognized among the global challenges especially in the era of climate change. Kampala city has faced a number of flooding events in the past that have led to serious damages in many low lying areas of the city. A study was conducted in one of the flood prone areas in Kampala city to understand the community perspectives on flood occurrence and weather forecasting in the area. The main objective was to understand community perceptions on flood occurrence as well as use of weather information for early warning. Data was collected from 400 respondents using a structured questionnaire as well as focused group discussions. Results from the survey revealed that 99.8% of the respondents reported rainfall intensities to have increased in the last 5 years and as a result, 96% of these respondents stated floods as the main climate risk in the area. The most common impacts of floods in the community included loss of property, lack of safe and clean water and disease outbreaks among others. In response to floods, the community identified use of raised tables or stands and use of stones in the compound as the main coping mechanisms employed in the community. In terms of weather alerts, only 22% of respondents reported receiving the weather alerts issued by the Uganda National Meteorological Authority (UNMA). Of those who receive the alerts, 91% hear the information mainly on television. Therefore, the study proposes strengthening of collaboration between UNMA and the local leaders through the Kampala Capital City Authority (KCCA) for provision of earlier weather forecasts that help reduce the negative flood impacts among communities.   Key words: Weather alerts, flood forecasting, changing climate and community perspectives.

Predicting heave and surge motions of a semi-submersible with neural networks

Real-time motion prediction of a vessel or a floating platform can help to improve the performance of motion compensation systems. It can also provide useful early-warning information for offshore operations that are critical with regard to motion. In this study, a long short-term memory (LSTM) -based machine learning model was developed to predict heave and surge motions of a semi-submersible. The training and test data came from a model test carried out in the deep-water ocean basin, at Shanghai Jiao Tong University, China. The motion and measured waves were fed into LSTM cells and then went through several fully connected (FC) layers to obtain the prediction. With the help of measured waves, the prediction extended 46.5 s into future with an average accuracy close to 90%. Using a noise-extended dataset, the trained model effectively worked with a noise level up to 0.8. As a further step, the model could predict motions only based on the motion itself. Based on sensitive studies on the architectures of the model, guidelines for the construction of the machine learning model are proposed. The proposed LSTM model shows a strong ability to predict vessel wave-excited motions.

Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China

Prediction of landslide movement is an efficient approach in the reduction in landslide risk. However, it is also a tough task due to the scientific challenges in forecasting a sophisticated natural disaster. This paper proposes a VMD-MIC-M-KELM (variational mode decomposition-maximum information coefficient-multi-kernel extreme learning machine) technique for prediction of landslide movements. The original displacement is first decomposed into a predefined number of components by VMD. Then, the triggers of each component are selected based on MIC between subseries and influencing factors. The decomposed terms are predicted by M-KELM respectively via k-fold cross-validation. Finally, predicted total displacement is achieved by summing up all forecasting subseries. A case study of Miaodian landslide (China) is presented for validation of the developed model. The verification results demonstrate the higher ability of the approach to forecast monthly displacement for periods up to 12 months as compared to the Poly-KELM and SVR models. Thus, improved monthly predictions may be achieved with constantly updated datasets from the monitoring system, which would offer reliable information for early warning of landslide.

Overview of Earthquake Early Warning Information Release System in Fujian Province, China

Earthquake early warning is a technical method that can effectively reduce earthquake disasters. In May 2018, the Fujian Earthquake Agency officially provided information services such as earthquake early warning and earthquake quick report to the public through dedicated receiving terminal and the smart phone application named “Fujian Earthquake Early Warning”. This article briefly introduces the preliminary work of the Fujian Earthquake Early Warning Data Processing System, and focuses on the construction of the Fujian Earthquake Early Warning Information Release System. Finally, taking the M6.4 earthquake in Yilan in 2019 as an example, the actual effect of the earthquake early warning information service is introduced.

A new early-warning technology based on microseism and deep sliding force comprehensive monitoring for high-steep slope landslide disaster

The accurate and efficient identification of potential slip surfaces of high-steep slopes, monitoring of deep sliding forces, and multi-source monitoring information fusion analysis and processing are the key technical problems of slope monitoring and early-warning. A new method for accurately and efficiently identifying the potential slip surfaces of high-steep slopes was proposed to solve these problems. A refined high-constant resistance energy-absorption anchor cable monitoring device was also developed, and multi-source monitoring and early-warning information fusion discrimination criteria were established. A set of new early-warning technology for open-pit slope landslide disasters based on the combination of microseism and deep sliding force monitoring was formed. This technology was used to successfully warn about landslide disasters twice or three days in advance in the demonstration mine, which effectively guided the safety management and emergency decision making. The successful development and application of this new early-warning technology provide a new idea for the safety monitoring and control technology of high-steep slopes in China.

Prediction of online topics’ popularity patterns

Popularity prediction of online contents is always a tool of emergency management, business decision-making, and public opinion monitoring. Most previous work has made efforts to predict the volumes or levels of popularity, but patterns of popularity evolution are remaining largely unexplored. Actually, topic popularity patterns can offer more detailed information for event detection and early warning. In this article, we proposed an effective method to discover and predict the popularity patterns of topics on the Internet which combined clustering and classification models. This method does not rely on the early time data of topic propagation, so it can predict the future popularity pattern at the initial stage of topic releasing. First, we chose a time series clustering algorithm K-SC to obtain basic types of topic popularity patterns. Then, through acquiring and evaluating multiple features related to the topics including publisher features, outward characteristics of content and textual ones, we built the prediction model of topic popularity patterns based on machine learning methods. The experimental results show that it is suitable to cluster four basic patterns of topic popularity from the experimental data. What’s more, making use of certain initial characteristics, Decision Tree model can effectively predict the popularity pattern of a newly released topic, with an accuracy of 89.4%.

Quantitative description of infrared radiation characteristics of preflawed sandstone during fracturing process

Previous studies show that infrared radiation temperature (IRT) abnormalities are always accompanied by the crack development in rocks under external loads. In this context, experiments were conducted on preflawed sandstone to investigate the infrared radiation characteristics during failure process. Two indicators were defined herein, i.e. coefficient of variation of IRT (CVIRT) and skewness of IRT (SIRT). The regression analysis shows that the IRT probability distributions during loading process fit the Gaussian model. The variations in the CVIRT are characterized by four stages: primary stage, steady stage, accelerating stage and post-peak stage. Besides, the variations in the SIRT are divided into three stages: primary stage, steady stage and failure and post-peak stage. The precursor point for preflawed rock failure is identified based on the CVIRT–time curve, with average precursor point of 83% of the peak stress. Compared with other IRT indicators, the proposed two IRT indicators have higher sensitivity to IRT abnormalities during failure process. Furthermore, the connection between the IRT indicators and the rock fracturing was investigated to interpret the IRT indicator abnormalities. Based on the Verhulst inverse function, a new quantitative model was presented to describe the primary stage, steady stage and accelerating stage of the CVIRT–time curve. The results obtained in this study can provide early-warning information for rock failure prediction.

Efficiency Evaluation of Air-defense Early-warning Detection Based on Communication Support

In order to quantitatively analyze the impact of communication support capability on early-warning detection efficiency under electronic jamming condition, based on the study of the air-raid targets searching process, this paper build the detection probability calculating model of target indicating radar by evaluating the jamming efficiency of the air intelligence informing communication. Combined with the typical combat scenario, analysis of examples simulation is done and the results show that the jamming effect of using airborne jamming is better than using subgrade jamming on communication and the target indicating error which is caused by early-warning information does not affect the improvement of early warning detection capability.

Smallholder wheat farmers’ commercialization level and its determinants in northwestern Ethiopia

This study analyzes the commercial engagement of smallholder farmers in wheat commercialization at the farm level and the factors affecting this. A stratified three stage sampling technique was used to collect data from 195 smallholder wheat farmers. Descriptive and Tobit models were employed to analyze the collected data. The result of study identified that on average smallholder wheat farmers were relatively lacking in commercial activities as the mean commercialization index was found to be 0.21. The age category of a household head, quantity produced, distance to the nearest market, formal extension services, membership of farmers’ cooperatives, climatic (early warning and preparedness) information, and perception towards lagged market prices were found to determine significantly the commercialization activities of wheat farmers. The commercialization level of wheat farmers can be increased by improving their understanding of commercial farming, strengthening the marketing system (accesses and infrastructure), increasing the number of adequate extension contacts, timely climatic information, making market relationships (forward and future contract) available, and increasing farmers involvement in farming cooperatives.

Fishers’ perceptions and adaptation on climate change in northeastern Taiwan

The impacts of climate change on Taiwanese marine capture fisheries have been observed in several studies. To help decision makers develop appropriate, fishery-related measures to enhance the resilience or social adaptation capacity to respond to climate change, an analysis of fishers’ perceptions and their adaptation is needed. This study selected Keelung City, New Taipei City, and Yilan County as case studies for analyzing fishers’ perceptions on climate change in northeastern Taiwan. To meet research objectives, we conducted a questionnaire survey to interview 273 fishers who have experiences of capture fishing. Then, descriptive statistics, factor analysis, and logistic regression were used for empirical analysis. The results indicated that a total of 81% of respondents might adapt their fishing behavior in response to climate change. The empirical results show that the respondents who recognized the importance of risk controls and the impacts on marine physical environments might change fishing behaviors in response to climate change. Also, the respondents who have substantial experience fishing might adapt their fishing behaviors in response to climate change. Elderly respondents might not adapt their fishing behaviors in response to climate change. Based on the findings, three management recommendations for related policies are offered: (1) a modified incentive program to attract younger labor forces to join the fishing industry; (2) an early-warning or weather information system; and (3) marine resources management measures. The results provide a valid foundation for management agencies of coastal and offshore fisheries under climate change.

Microdroplet-captured tapes for rapid sampling and SERS detection of food contaminants

Simple, effective, and rapid detection of chemically relevant hazards is a highly desirable research goal, which can provide early-warning information to improve the patient-care outcomes for public health. Here, we introduce a microdroplet-captured tape toward rapid SERS screening of food contaminants. The dominant sensing unit lies on functionalized microwell in conductive carbon tapes, which is simply prepared by physical punching, magnetron sputtering and electrochemical deposition of Au nanodendrites. The tape-based sensors not only possess highly branched Au nanodendrites in microwell for promoting SERS activity, but also enable anchoring the microdroplets via direct dip-pulling from pristine analytes solutions upon sticky incorporated on a glove. Early-warning SERS detection of food contaminants including Sudan-1, thiram, and thiabendazole from the real samples can be achieved by such simple sampling method. These tape-based sensors with a facile operation module and accessible signal read-out represent an innovative point-of-care testing (POCT) device for forensic, military, consumer protection, environmental monitoring, and food safety applications.

A novel early-warning method for atypical outbursts disasters in mines: extraction of indexes from gas concentration data for the early warning of atypical outbursts

Accurate identification and forecasting of typical outbursts in mines remain scientific problems needed to be tackled. Based on the related data of the occurrence of atypical outbursts, this study employed theoretical analysis, laboratory test, mathematical treatment, and commercial test for investigating the physical and mechanical properties of coal that resulted in an atypical outburst, the quantitative conditions of the occurrence of atypical outbursts and the extraction of indexes from gas concentration data for early warning. Results show that the physical and mechanical properties of the coal rock where an outburst occurred fell in between the properties of the coal rock resulting in a rock burst and the properties of the coal rock resulting in coal and gas outburst, and an atypical outburst was easily triggered under high crustal and high gas pressure environment; gas extraction leads to the enhancement of coal rock mass brittleness and bump proneness. Based on specific physical and mechanical characteristics of the coal that results in an atypical outburst, the related early-warning information in gas concentration data that can reflect the evolution process of atypical outburst disasters were mathematically extracted, and the mathematical model of early-warning indexes was established.