Randomized Machine Learning Algorithms to Forecast the Evolution of Thermokarst Lakes Area in Permafrost Zones

Automation and Remote Control. 2023. Vol. 84. No. 1. P. 64–81.

Yu. A. Dubnov, A. Yu. Popkov, Polishchuk V. Y., Sokol E. S., Melnikov A. V., Polishchuk Y. M., Yu. S. Popkov

Randomized machine learning focuses on problems with considerable uncertainty in data and models. Machine learning algorithms are formulated in terms of a functional entropylinear programming problem. We adapt these algorithms to forecasting problems on an example of the evolution of thermokarst lakes area in permafrost zones. Thermokarst lakes generate methane, a greenhouse gas affecting climate change. We propose randomized machine learning procedures using dynamic regression models with random parameters and retrospective data (climatic parameters and remote sensing of the Earth’s surface). The randomized machine learning algorithm developed below estimates the probability density functions of model parameters and measurement noises. Randomized forecasting is implemented as algorithms transforming the optimal distributions into the corresponding random sequences (sampling algorithms). The randomized forecasting procedures and technologies are trained, tested, and then applied to forecast the evolution of thermokarst lakes area in Western Siberia.

Keywords: информационная энтропия, дистанционное зондирование, Remote Sensing, information entropy, randomized forecasting, thermokarst lakes, рандомизированное машинное обучение, рандомизированное прогнозирование, randomized machine learning, термокарстовые озера