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

Pavel M. Kikin, Alexey A. Kolesnikov, Alexey M. Portnov, Denis V. Grischenko

Результат исследований: Научные публикации в периодических изданияхстатья в журнале по материалам конференциирецензирование

1 Цитирования (Scopus)

Аннотация

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.

Переведенное названиеNatural language processing systems for data extraction and mapping on the basis of unstructured text blocks
Язык оригиналарусский
Страницы (с-по)53-61
Число страниц9
ЖурналInterCarto, InterGIS
Том26
DOI
СостояниеОпубликовано - 2020
Событие2020 International Conference on GI Support of Sustainable Development of Territories - Moscow, Российская Федерация
Продолжительность: 28 сен 202029 сен 2020

Предметные области Scopus

  • Компьютерные технологии в науках о земле
  • Процессы поверхности земли
  • Геофизика
  • География, планирование и развитие

Ключевые слова

  • Ecosystems
  • Forecasting
  • LSTM
  • SARIMAX
  • Spatio-temporal indicators

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