Development of a system for monitoring and managing climate-dependent process risks based on hidden Markov models (using grain crop yields as an example)

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.359355

Keywords:

hidden Markov models, probabilistic risk monitoring, TO-BE architecture, process management

Abstract

This study focuses on climate-dependent production processes, in particular grain crop yields, under conditions of climatic variability and uncertainty in Northern Kazakhstan. The problem addressed is the low effectiveness of deterministic risk monitoring approaches due to limited predictive power and the lack of formalized risk criteria, which leads to unreliable decision-making under uncertainty.

The results include the development of a three-state hidden Markov model (S0–S2) and a TO-BE architecture for continuous risk monitoring and decision support. The model enabled the identification of latent climatic regimes and probabilistic assessment of risk states for 2025. The highest probability of an unfavorable regime was observed in Korgalzhyn (61.2%) and Ereymentau (58.8%), while Arshaly (42.9%) and Zhaksy (38.1%) showed moderate risk levels. The Brier score ranged from 0.106 to 0.199, confirming acceptable calibration of probabilistic estimates.

The key feature of the approach is the representation of climate-dependent processes as transitions between latent probabilistic states, allowing the capture of temporal dependencies (climate memory) and the persistence of unfavorable conditions. Unlike deterministic models, the proposed framework enables dynamic risk tracking through continuously updated probability estimates integrated into a monitoring loop.

The advantage of the approach lies in combining probabilistic modelling with an operational architecture, where risk probabilities serve as formalized decision-support signals. The results can be applied in early warning systems and digital monitoring platforms using remote sensing and IoT

Author Biographies

Dulat Kali, Astana IT University

Astana IT University

Nurzhamal Kashkimbayeva, Astana IT University

PhD

School of Software Engineering

Ayan Kemel, Astana IT University

Master of Science (Computer engineering and software)

School of Software Engineering

Botagoz Mirzagalikova, Astana IT University

Master of Science (Robotics)

School of Software Engineering

Zhuldyz Basheyeva, Astana IT University

PhD

School of Software Engineering

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Development of a system for monitoring and managing climate-dependent process risks based on hidden Markov models (using grain crop yields as an example)

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Published

2026-04-30

How to Cite

Kali, D., Kashkimbayeva, N., Kemel, A., Mirzagalikova, B., & Basheyeva, Z. (2026). Development of a system for monitoring and managing climate-dependent process risks based on hidden Markov models (using grain crop yields as an example). Eastern-European Journal of Enterprise Technologies, 2(3 (140), 15–26. https://doi.org/10.15587/1729-4061.2026.359355

Issue

Vol. 2 No. 3 (140) (2026): Control processes

Section

Control processes

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Copyright (c) 2026 Dulat Kali, Nurzhamal Kashkimbayeva, Ayan Kemel, Botagoz Mirzagalikova, Zhuldyz Basheyeva

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