Finite-Difference Models Application for Short-Term Forecasting of the Natural Resource Potential of the Perm Region

Sirotina, Natalia; Kopoteva, A.V.; Zatonskiy, A.V.

doi:10.14529/ctcr210215

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(1 citation statement)

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“…Тем не менее нам не удалось обнаружить ни одного исследования, посвященного применению моделей машинного обучения для прогнозирования совокупного природно-ресурсного потенциала территории, что и определяет актуальность данной работы. Наши собственные исследования ПРП Пермского края [15] базируются на множественной линейной модели и ее модификациях, которые не обеспечивают достаточного качества прогнозирования. Рассмотрим возможность использования некоторых методов машинного обучения с учителем для повышения качества прогнозирования уровня природно-ресурсного потенциала Пермского края.…”

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Perm Region Natural Resource Potential Forecasting Using Machine Learning Models

Kopoteva¹,

Maksimov²,

Sirotina³

2021

CTAC&R

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In the article we consider a complex indicator of region natural resource potential modeling and forecasting quality improvement using different machine learning models. Problem under consideration importance is determined by the fact that the models traditionally used for these purposes demonstrate either low quality, or high configuration and parameters evaluation difficulty. The aim of the study is determination of machine learning models that provide the optimal values of various modeling quality metrics. Materials and methods. For this study purposes we considered the multiple linear regression, decision tree, random forest, gradient boosting and multilayer perceptron mo¬dels. We used the determination coefficient R2, the root mean square error of modeling RMSE, the average absolute error of modeling MAE, and the relative error of prediction for 1 and 2 time intervals as quality metrics. This study is based on data of the complex indicator of the Perm Region natural resource potential and the system of its determining factors in the time interval from 2001 to 2018. We evaluate models and calculate quality metrics using Pandas and Scikit-learn Python libraries in Jupiter Notebook environment. Results. According to our research the classical multiple linear regression model demonstrates the worst results for all quality metrics under consideration. The decision tree model demonstrates determination coefficient maximum value and minimum root mean square error and mean absolute error. Minimum relative forecasting error for 2017 is provided by the gradient boosting model, for 2018 – by the multilayer perceptron model. Conclusion. Our study allows us to affirm that nonlinear machine learning models for the task of region natural resource potential modeling and forecasting demonstrate better approximating and predictive properties compared to multiple linear regression and thus can be used to improve the quality of natural resource management.

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