Search results

In the past, predictions of impending natural disasters have captured public attention for a variety of reasons. When such predictions gain momentum, they can have serious consequences for those to whom the public turns for advice, information, and comfort. Over the next few years, it is anticipated that predictions anchoring on the arrival of the millennium will appear and gain notoriety. Research has shown, however, some commonalties among predictions and predictors, as well as how to effectively transmit risk information to the public. The purpose of this paper is to provide those responding to unconventional predictions of imminent disaster a framework within which to guide their actions.

The work presented here is based on the assumption that all non‐linear properties, appearing in the evolution of magnetic fluids, are a complex system. Then, the establishment of a relationship between the structural changes in the geomagnetic field and the occurrence of earthquakes by employing the so‐called infrastructural informational analysis is undertaken. From the authors’ previously published serial work, it was found that the “zero isotropic zone” is a “discernible characteristic indication” which can be applied to the predictions of earthquakes with great practical effect.

This paper aims to describe two Web-based technologies of geographic information systems (GIS) to be used in monitoring and analysis of environmental processes, proposed by the authors. The technologies analyze the temporal aspect of the process together with the spatial aspect, which defers them from most other works on environmental processes, as these are usually limited either to spatial statistics or to temporal statistics. The approach is instrumental in dynamically finding the relationships between the processes and predicting critical incidents.

Often, the study of natural processes is limited to the analysis of their spatial properties presented by individual time series. The principal idea of this approach consists in supplementing this traditional analysis with the analysis of time fields. In this way, the authors are able to analyze temporal and spatial properties of environmental processes together.

The paper presents two technologies which provide the analysis of spatial and temporal data obtained in natural environment monitoring. The discussed spatio-temporal data mining methods are shown to enable the research into environmental processes, and the solution of practical issues of critical situation forecasts.

The paper discussed Web-based GIS technologies for the analysis of the temporal aspect of the environmental process together with the spatial aspect. Application examples demonstrate the ability of this approach to find the relationships in dynamics of the processes and to predict critical incidents.

The purpose of this paper is to offer two Web-based platforms for systematic analysis of seismic processes. Both platforms are designed to analyze and forecast the state of the environment and, in particular, the level of seismic hazard. The first platform analyzes the fields representing the properties of the seismic process; the second platform forecasts strong earthquakes. Earthquake forecasting is based on a new one-class classification method.

The paper suggests an approach to systematic forecasting of earthquakes and examines the results of tests. This approach is based on a new method of machine learning, called the method of the minimum area of alarm. The method allows to construct a forecast rule that optimizes the probability of detecting target earthquakes in a learning sample set, provided that the area of the alarm zone does not exceed a predetermined one.

The paper presents two platforms alongside the method of analysis. It was shown that these platforms can be used for systematic analysis of seismic process. By testing of the earthquake forecasting method in several regions, it was shown that the method of the minimum area of alarm has satisfactory forecast quality.

The described technology has two advantages: simplicity of configuration for a new problem area and a combination of interactive easy analysis supported by intuitive operations and a simplified user interface with a detailed, comprehensive analysis of spatio-temporal processes intended for specialists. The method of the minimum area of alarm solves the problem of one-class classification. The method is original. It uses in training the precedents of anomalous objects and statistically takes into account normal objects.

The purpose of this paper is to conduct performance evaluation of eight main scientific data sharing platforms in China and find existing problems, thus providing reference for maximizing the value of scientific data and enhancing scientific research efficiency.

First, the authors built an evaluation indicator system for the performance of scientific data sharing platforms. Next, the analytic hierarchy process was employed to set indicator weights. Then, the authors use experts grading method to give scored for each indicator and calculated the scoring results of the scientific data sharing platform performance evaluation. Finally, an analysis of the results was conducted.

The performance evaluation of eight platforms is arranged by descending order by the value of F: the Data Sharing Infrastructure of Earth System Science (76.962), the Basic Science Data Sharing Center (76.595), the National Scientific Data Sharing Platform for Population and Health (71.577), the China Earthquake Data Center (66.296), the China Meteorological Data Sharing Service System (65.159), the National Agricultural Scientific Data Sharing Center (55.068), the Chinese Forestry Science Data Center (56.894) and the National Scientific Data Sharing & Service Network on Material Environmental Corrosion (Aging) (52.528). And some existing shortcomings such as the relevant policies and regulation, standards of data description and organization, data availability and the services should be improved.

This paper is mainly discussing about the performance evaluation system covering operation management, data resource, platform function, service efficiency and influence of eight scientific data sharing centers and made comparative analysis. It reflected the reality development of scientific data sharing in China.

Published by the Science and Technology Division of the Library of Congress since 1972, the Tracer Bullet series is an underused reference source available in many library and government documents collections. The Tracer Bullets cover a wide variety of subjects in the natural and physical sciences and technology. Each one is devoted to a specific topic and is designed “to help a reader begin to locate published material on a subject about which he or she has only general knowledge.” Developed in the style of a library pathfinder, each explores the resources available, listing texts, handbooks, encyclopedias, dictionaries, bibliographies, government documents, and journal articles. Addresses and telephone numbers of relevant organizations are also included as are appropriate Library of Congress subject headings to use in locating additional material.

Using the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied.

In the range of 0.5–10.0 s after the P-wave arrival, the prediction time window was established at an interval of 0.5 s. 12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning (EEW) magnitude prediction model (SVM-HRM) for high-speed railway based on SVM.

The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm. Results show that at the 3.0 s time window, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τ_c method and P_d method. The overestimation of small earthquakes is obviously improved, and the construction of the model is not affected by epicenter distance, so it has generalization performance. For earthquake events with the magnitude range of 3–5, the single station realization rate of the SVM-HRM model reaches 95% at 0.5 s after the arrival of P-wave, which is better than the first alarm realization rate norm required by “The Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 to 5, 5 to 7 and 7 to 8, the single station realization rate of the SVM-HRM model is at 0.5 s, 1.5 s and 0.5 s after the P-wave arrival, respectively, which is better than the realization rate norm of multiple stations.

At the latest, 1.5 s after the P-wave arrival, the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate, which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

Based on the theory of blown‐ups, as described by OuYang in 1995 and 1994, for nonlinear dynamic systems on general pansystems transformation, optimization and panderivatives, etc., in this paper, we employ the method, developed in OuYang (1994), to show that the blown‐ups of the nonlinear heat conductive equation is similar to the evolution of observable ground temperature “currents”. According to this analysis, we conduct a simulation based on the historical data of Tang Shan Earthquake in 1976. The simulation results show that: the blown‐ups of the ground temperature “currents” around the earthquake area can be applied to predict forthcoming earthquakes. As for the most disastrous Tang Shan Earthquake, the prediction time is about five months in advance. If the ground temperature “current” that embodies the earth’s crust movement satisfies the unintegrable panderivative equation, we can with enough information demonstrate blown‐up mechanism and forecast relevant earthquakes.

Until the Loma Prieta earthquake of 17 October 1989, also known as the “World Series earthquake” or the “San Francisco earthquake,” many of us may have considered earthquakes a remote danger. But instantaneous television transmission from the interrupted World Series game and frightening images of the collapsed Cypress Viaduct and the burning Marina district transformed this incident from a distant disaster into a phenomenon that touched us all. The Loma Prieta earthquake was followed in December 1990 by the inaccurate but widely publicized New Madrid earthquake prediction. Despite its inaccuracy, this prediction alerted the public to the fact that the largest earthquake ever to have occurred in the United States occurred not in California or Alaska, but in Missouri, and that a large earthquake could occur there again. Americans are discovering that few places are immune to the possibility of an earthquake.

The matter of predicting disasters has always been one of the hottest and most challenging tasks in geology. Earthquakes are among the most destructive ones among all the natural hazards. Occurring often without any warning, they are the most feared and unpredictable natural phenomena. In recent years with the emergence of new remote sensing instruments and techniques, geologists interested themselves to define accurate and reliable procedures to foresee disasters using this new technology. This paper aims to examine some of the data that have been used so far in earthquake prediction as well as cheap, relevant remotely sensing and geographic information systems methods to acquire and manipulate data.

Earthquakes are not the same in terms of origins, places (depth) and effects. So after having a brief look at the earthquakes, this paper examines the data that can be used for predicting earthquakes and reviews some of the remote sensing methods used to predict tectonic earthquakes. According to the types of measurements, remote sensing methods can be categorized in three main types; crust displacement, thermal and electromagnetic detecting techniques. Regarding the area's geological characteristics, satellites with optical and/or synthetic aperture radar sensors applications in prediction of large‐scale natural disasters will be discussed.

Presentation of the definitions and characteristics of earthquakes, categorized representation of the types of data used in this field as well as the types and names of the ground, aerial‐ and space‐borne data providers are the most important products of this review paper.

This method, if fully and systematically conducted, can be the cornerstone of an earth‐predicting system.