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February 25, 2026Korean Journal of Remote SensingOpen Access

Enhanced Landslide Susceptibility Assessment in Angren, Uzbekistan using FR Feature Selection and CNN-GWO Hyperparameter Optimization

Kadirhodjaev Azam Alisherovich Bimurzaev Gany Amirgalievich Liadira Kusuma Widya Arief Rizqiyanto Achmad Melibaev Jasur Malik ugli Abdullaev Ganisher Bektosh ugli Sooyeon Han Saro Lee

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Key Points

This research aims to enhance the accuracy of landslide susceptibility assessments in Angren, Uzbekistan.
Utilized feature selection methods to identify key factors affecting landslide susceptibility.
Applied convolutional neural networks (CNN) for predictive modeling.
Employed grey wolf optimization (GWO) for hyperparameter tuning to improve model performance.
Achieved higher accuracy rates in predicting landslide occurrences compared to traditional methods.
Demonstrated the effectiveness of machine learning techniques in geological hazard assessments.

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Implication

Research demonstrates improved landslide risk assessment in Uzbekistan using advanced algorithms, suggesting better prediction methods.

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Cite This Study

Alisherovich et al. (2026) studied this question.

synapsesocial.com/papers/69a134b8ed1d949a99abe402 https://doi.org/10.7780/kjrs.2026.42.1.1

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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