ON THE METHOD OF DETERMINING LEARNING DESCRIPTIONS TO FORECAST NATURAL DISASTERS WITH THE PATTERN RECOGNITION SYSTEM

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.

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ON ONE APPROACH OF FORECASTING NATURAL DISASTERS WITH THE SYSTEM OF PATTERN RECOGNITION WITH LEARNING

World Science ◽

10.31435/rsglobal_ws/28022021/7447 ◽

2021 ◽

Author(s):

Nelly Tkemaladze ◽

Giorgi Mamulashvili

Keyword(s):

Pattern Recognition ◽

Initial Data ◽

Natural Disasters ◽

Natural Disaster ◽

Data Bases ◽

The Third ◽

Using Data ◽

And Control ◽

Fixed Region ◽

The Given

There are a number of recognition problems in different fields that can be solved with the system of pattern recognition with learning – SPRL elaborated by us. The problem of forecasting natural disasters (floods, mudslides) in the given year, the fixed region, and the period belongs to it. To solve it, it is set in the terms of pattern recognition with learning according to which it is necessary to pre-determine the learning descriptions in the same region of the previous years using data of the previous 12 months of the period. From learning descriptions, firstly are separated control descriptions, then the variants of learning and learning recognizable descriptions. Besides, it is necessary to determine descriptions in year, in the same region using data of the same previous period of the (the first model). After transformation and increasing the informativity of the learning descriptions, the knowledge and data bases are determined for learning recognizable and control descriptions in relation to the variants and classes (the second model). Using them, one decision is made on belonging to the respective class for learning recognizable descriptions, but for control descriptions – the primary decisions according to the number of variants, and then on their basis – one decision. Exactly according to the results of the recognition of control descriptions a decision is made on the occurrence (non-occurrence) of a natural disaster in the same region and period (the third model). The article discusses the arguments related to this fact. This model considers the correction of data bases with respect to variants and classes, also, defines the effectiveness of working of the SPRL and its detector of trust. Considering the specifics of forecasting, the initial data of at least 5 years are required to select the best knowledge and data bases with the use of which a disaster should be forecasted.

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