Methodological aspects of natural territories zoning using machine learning

by

D.O. Krivoguz, R.V. Borovskaya

Azov Sea Research Fisheries Institute (FSBSI “AzNIIRKH”), Kerch, Sverdlova St., 2

E-mail: krivoguz_d_o@azniirkh.ru

DOI: 10.33075/2220-5861-2020-1-14-20

UDC 544.623

Abstract:

     The article discusses a modern approach to natural territories zoning by machine learning method. Zoning of the territory is based on an integrated assessment of the quality of environmental indicators, taking into account the maximum possible number of factors that can fully describe the properties and features of the analyzed territory. The purpose of the work is to form a modern mathematical methodology based on machine learning methods, capable of significantly improving modern approaches of any territory zoning. The article analyzes current trends in zoning for various territories.

     Authors have analyzed the zoning algorithm using clustering, and also highlighted its main stages, which include obtaining and normalizing data, determining the propensity of a dataset to be divided into sets, identifying the optimal number of clusters and zoning. It was concluded that the effectiveness of any clustering algorithm is determined by its achievement of the compactness hypothesis, which consists in the fact that similar objects are much more likely to be in the same class than in different ones. The authors highlighted both the strengths and weaknesses of this approach. The authors consider such qualities as objectivity, accuracy, simplicity of modifiability and settings as positive ones while the strong dependence on the quality of the data of carried out standardization is considered to be negative, which, with any significant deviations in these aspects, can lead to a significant distortion of the results.

Keywords: territories zoning, fishery zoning, machine learning, environmental problems, clustering, data normalization.

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[IEEE] D. O. Krivoguz and R. V. Borovskaya, “Methodological aspects of natural territories zoning using machine learning,” Monitoring systems of environment, no. 1, pp. 13–20, Mar. 2020.

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