| Citation: | MO Deguo，ZHENG Guangming，WANG Chao，et al. Early identification and susceptibility assessment of landslide hazards in southern Dengfeng, Henan Province[J]. Bulletin of Geological Science and Technology，2026，45（4）：119-132 doi: 10.19509/j.cnki.dzkq.tb20250169
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The southern region of Dengfeng City in Henan Province lies in the transitional zone between the Songshan Mountains and the Middle and Lower reaches of the Yellow River plain. Complex topographic and geological conditions in this region lead to frequent landslide hazards, posing serious threats to regional production safety and residents' lives. Conducting early identification of landslide hazards and high-precision susceptibility assessment is practically significant for the prevention and control of regional geological hazards.
This study applied optical remote sensing and small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the early identification of landslide hazards. Twelve key evaluation factors, including elevation, slope, and lithology, were selected. Landslide susceptibility assessment was carried out based on the information value model and machine learning methods (artificial neural network, random forest, and stacking ensemble model). Additionally, slope units were used to optimize the output of evaluation results.
The results showed that: (1) A total of 33 landslide hazard sites were identified through multi-source remote sensing interpretation and field verification. They were mainly distributed in the central, southwestern, and southeastern parts of the study area, and their spatial distribution was significantly correlated with terrain slope, weak lithological layers, and human engineering activities. (2) Landslide susceptibility in the study area showed a distribution pattern of lower susceptibility in the north and higher susceptibility in the south. The stacking ensemble model achieved the highest prediction accuracy with an area under the curve (AUC) value of 0.96, which was significantly better than single models and the traditional information value model. The high susceptibility zone accounted for only 8.60% of the total area but captured 87.72% of the landslide samples.
The technical framework of multi-source remote sensing identification combined with ensemble learning evaluation constructed in this study provides high-precision data support and a technical paradigm for accurate prevention and control of landslide risks in the southern region of Dengfeng. It also verifies the significant advantages of ensemble learning for landslide susceptibility assessment in complex terrain areas.
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