Abstract
To forecast natural disasters (floods, mud-slides) in the fixed region and in period T0 with SPRL – the System of Pattern Recognition with Learning (elaborated by us) it is necessary to have the data of the previous 12 months of period T0 and learning descriptions (LDs). To identify this latter, the fact of occurrence or non-occurrence of disasters in the same region and the period T0 should be known in other years and also, the above mentioned 12- month date for each year. Determining LDs based on them is the aim of the article. For this purpose, the method which will be included in the first model of the SPRL is elaborated. The SPRL comprises: 1) preliminary elaboration of the initial information, 2) learning and 3) recognition models. This system is implemented on a PC. It is verified on the basis of the real data to recognize objects of different classis. Primary, additional and formal additional parameters are determined in the method given in the article. On the basis of their values in correlation with the aforementioned 12 months two matrices are determined. The first of them corresponds to the fact of occurrence of disasters and the second one – of non-occurrence. By using these parameter values given in these matrices LDs will be determined. The best LDs will be given to the learning model of the SPRL for transformation and increasing of informativity. Based on the LDs obtained after the transformation, the learning model will make knowledge and data bases.
Highlights
One of the important problems is forecasting the natural disasters [1,2]
In case of objects, learning description [5,6] is the sequence of parameter values of an object for which the class, the object presented by this sequence belongs to, is known beforehand
To determine learning descriptions, besides the method given in this article, only the two models of the System of Pattern Recognition with Learning (SPRL) are used
Summary
Tkemaladze, PhD, Senior Scientific Worker, Department of Mathematical Cybernetics Georgia, Tbilisi, V. Violeta Jikhvashvili, MA, Department of Mathematical Cybernetics Georgia, Tbilisi, V. Giorgi Mamulashvili, MA, Department of Mathematical Cybernetics Georgia, Tbilisi, V.
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