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ORIGINAL RESEARCH
AI-Driven Spatial and Temporal Analysis
of Ecological Assessment in the West
Liao River Basin (2010-2070), China
1
School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010051, China
2
National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian
Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology, Hohhot 010021, China
3
Inner Mongolia Key Laboratory of Water Resource Protection and Utilization, College of Water Conservancy and Civil
Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Submission date: 2024-07-08
Final revision date: 2024-10-19
Acceptance date: 2024-11-10
Online publication date: 2025-01-29
Publication date: 2026-01-29
Corresponding author
Xiaoming Su
School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010051, China
School of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010051, China
Pol. J. Environ. Stud. 2026;35(1):327-343
KEYWORDS
ecological securitypressure-state-response (psr) modelartificial intelligence predictiontimeseries analysisWest Liao River Basin
TOPICS
ABSTRACT
Amid escalating environmental pressures and dynamic socio-economic changes, current research on
ecological security often needs to provide comprehensive predictive models that effectively incorporate
multivariate relationships and long-term trends. This study aims to fill this gap by employing the
Pressure-State-Response(PSR) framework, utilizing 18 indicators derived from meteorological, remote
sensing, soil, terrain, and socio-economic datasets to evaluate the ecological security of the West
Liao River Basin from 2010 to 2021. A transformer-based artificial intelligence model was developed
to predict time-series indicators from 2022 to 2070, enhancing the accuracy of trend, seasonality,
multi-scale, and multivariate relationship predictions. Our findings reveal that the Ecological Security
Index(ESI) remained within the “Generally secure” category, exhibiting a slightly declining trend with
values ranging between 0.478 and 0.499. Key obstacle factors identified include the proportion of the
non-agricultural population, power of agricultural machinery, effective irrigated area, GDP per capita,
and the proportion of cultivated land to land area. Compared to state-of-the-art models such as Informer,
LightTS, TimesNet, and Dlinear, our model demonstrates significant improvements in Mean Absolute
Error(MAE) of 1.04%, 4.09%, 3.22%, and 4.54%, respectively. This research provides critical insights
into the region’s management and enhancement of ecological security.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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