TY - JOUR

T1 - АНАЛИЗ И ПРОГНОЗИРОВАНИЕ ПРОСТРАНСТВЕННО-ВРЕМЕННЫХ ЭКОЛОГИЧЕСКИХ ПОКАЗАТЕЛЕЙ С ИСПОЛЬЗОВАНИЕМ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ

AU - Kikin, Pavel M.

AU - Kolesnikov, Alexey A.

AU - Portnov, Alexey M.

AU - Grischenko, Denis V.

N1 - Publisher Copyright: © 2020 Lomonosov Moscow State University. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - The state of ecological systems, along with their general characteristics, is almost always described by indicators that vary in space and time, which leads to a significant complication of constructing mathematical models for predicting the state of such systems. One of the ways to simplify and automate the construction of mathematical models for predicting the state of such systems is the use of machine learning methods. The article provides a comparison of traditional and based on neural networks, algorithms and machine learning methods for predicting spatio-temporal series representing ecosystem data. Analysis and comparison were carried out among the following algorithms and methods: logistic regression, random forest, gradient boosting on decision trees, SARIMAX, neural networks of long-term short-term memory (LSTM) and controlled recurrent blocks (GRU). To conduct the study, data sets were selected that have both spatial and temporal components: the values of the number of mosquitoes, the number of dengue infections, the physical condition of tropical grove trees, and the water level in the river. The article discusses the necessary steps for preliminary data processing, depending on the algorithm used. Also, Kolmogorov complexity was calculated as one of the parameters that can help formalize the choice of the most optimal algorithm when constructing mathematical models of spatio-temporal data for the sets used. Based on the results of the analysis, recommendations are given on the application of certain methods and specific technical solutions, depending on the characteristics of the data set that describes a particular ecosystem.

AB - The state of ecological systems, along with their general characteristics, is almost always described by indicators that vary in space and time, which leads to a significant complication of constructing mathematical models for predicting the state of such systems. One of the ways to simplify and automate the construction of mathematical models for predicting the state of such systems is the use of machine learning methods. The article provides a comparison of traditional and based on neural networks, algorithms and machine learning methods for predicting spatio-temporal series representing ecosystem data. Analysis and comparison were carried out among the following algorithms and methods: logistic regression, random forest, gradient boosting on decision trees, SARIMAX, neural networks of long-term short-term memory (LSTM) and controlled recurrent blocks (GRU). To conduct the study, data sets were selected that have both spatial and temporal components: the values of the number of mosquitoes, the number of dengue infections, the physical condition of tropical grove trees, and the water level in the river. The article discusses the necessary steps for preliminary data processing, depending on the algorithm used. Also, Kolmogorov complexity was calculated as one of the parameters that can help formalize the choice of the most optimal algorithm when constructing mathematical models of spatio-temporal data for the sets used. Based on the results of the analysis, recommendations are given on the application of certain methods and specific technical solutions, depending on the characteristics of the data set that describes a particular ecosystem.

KW - Ecosystems

KW - Forecasting

KW - LSTM

KW - SARIMAX

KW - Spatio-temporal indicators

UR - http://www.scopus.com/inward/record.url?scp=85099953333&partnerID=8YFLogxK

U2 - 10.35595/2414-9179-2020-3-26-53-61

DO - 10.35595/2414-9179-2020-3-26-53-61

M3 - статья в журнале по материалам конференции

AN - SCOPUS:85099953333

VL - 26

SP - 53

EP - 61

JO - ИНТЕРКАРТО/ИНТЕРГИС

JF - ИНТЕРКАРТО/ИНТЕРГИС

SN - 2414-9179

T2 - 2020 International Conference on GI Support of Sustainable Development of Territories

Y2 - 28 September 2020 through 29 September 2020

ER -