-isothermal stages to reduce the duration of the whole process, thus improving the thin-film production. To this purpose, “model-free prediction methods” are especially suited [ 5 ] because they allow the ‘prediction’ of the evolution of a solid state transformation
Thermogravimetry (TG) or methods of thermal analysis have been for a long time recognized as methods useful for prediction of stability of various materials (e.g., refs. [ 23 , 24 ]). It can be concluded that TG mass losses could detect also changes in the soil
The recent scientific advances in understanding the hierarchical nature of the lithosphere and its dynamics based on systematic monitoring and evidence of its space-energy similarity at global, regional, and local scales did result the design of reproducible intermediate-term middle-range earthquake prediction technique. The real-time experimental testing aimed at prediction of the largest earthquakes world-wide from 1992 to the present proved statistically a possibility of practical earthquake forecasting although of limited precision. In the first approximation, an accuracy of 1-5 years and 5-10 times the anticipated source dimension is achieved. Further analysis of seismic dynamics allows reducing the spatial uncertainty down to 1-3 source dimensions, although at the cost of additional failures-to-predict. Despite of limited accuracy a considerable damage could be prevented by timely knowledgeable use of the existing predictions and earthquake prediction strategies. The link of theoretical research in modeling earthquake sequences in frames of statistical physics on the one hand and instrumental and algorithm developments on the other hand help developing a new generation of earthquake prediction technique of higher accuracy.
Dramatic oods occurred in Central Europe in recent summers, Hungary having been seriously affected in its eastern part. Predictive approach based on modeling ood recurrence may be helpful in ood management. Summer oods are typically characterized by saturated catchment due to long-lasting heavy precipitation followed by a sudden extreme rainfall. In present work, an artificial neural network (ANN) models were evaluated for precipitation forecasting. A back propagation neural networks were trained with actual annual and monthly precipitation data from east Hungarian meteorological stations for a time period of 38 years. Predicted amounts are next-year-precipitation and summer precipitation in the next year. The ANN models provided a good with the actual data, and have shown a high feasibility in prediction of extreme precipitation.