SCIENCEGATE

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

2021 ◽
Author(s):
Nelly Tkemaladze
Giorgi Mamulashvili

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.

2013 ◽
Vol 44(4)
pp. 271-277
Author(s):
Simona Sacchi
Paolo Riva
Marco Brambilla

2019 ◽
Vol 125 ◽
pp. 01007
Author(s):
Mufidah Tartila
Supriatna
Masita Dwi Mandini Manessa
Yoanna Ristya

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