Role of information systems in ecology for achieving the Sustainable Development Goals (SDGs)

Document Type : Research Paper

Authors

1 Al-Farabi Kazakh National University UNESCO Chair for Sustainable Development Departament of Geography and Environmental

2 Laboratory of Hydrochemistry and Main Toxicology, JSC Institute of Geography and Water Safety, Al-Farabi Kazakh National University

3 Kazakh National Agrarian Research University, Faculty of «Engineering and Technology»

4 Kyrgyz National University named after Jusub Balasagyn

5 Tourism College of Kyrgyz National University named after Jusup Balasagyn

10.22124/cjes.2025.8912

Abstract

This research aimed to create and evaluate an integrated information system based on digital twins for monitoring vulnerable ecosystems in Kazakhstan and supporting the Sustainable Development Goals (SDGs). The research was conducted in three large ecosystems (semi-dry steppes, Aral Sea basin and nature reserves) by combining multi-source information including remote sensing (Landsat-8/9), Sentinel-1/2, IoT sensor networks (1,240 sensors) and artificial intelligence models. The analysis revealed alarming land degradation acceleration with the area of 32,150 km2 over five years (28.5% in the Aral Sea basin) and 23.1% decline of Saitaga population. The LSTM predictive model successfully identified crisis hotspots 6 months prior with precision 86.5% (κ = 0.79) and error RMSE = 0.11 ± 0.02 that was 37% more accurate than methods used in the past. Implementation of the system brought revolution in environmental administration, e.g., reduction in decision-making time by 80.5%, 33.2% improvement in allocation of conservation budget, and a rise in snow leopard population in the observed ranges by 19.5%. The monitoring cost also came down by 78.1% (savings of $3.68 million each year). This model, with its ability to monitor SDG 15.3.1 and SDG 6.3.2 key indicators in near real-time, is a good model for Central Asian nations.

Keywords

References

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Caspian Journal of Environmental Sciences
Volume 23, Issue 3
July 2025
Pages 611-617

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