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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.

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.