Modeling of landslides susceptibility prediction using deep belief networks with optimized learning rate control

Abstract

To overcome critical issues in landslide susceptibility modeling, a multifactor landslide susceptibility prediction model based on deep belief networks (DBN) with optimized learning rate control (LRC-DBN) was introduced in this study. The LRC-DBN model was applied to predict landslide susceptibility in Zhidan County, Shaanxi and its performance was compared against random forest, support vector machine and logistic regression models. The results show that the LRC-DBN model achieves a maximum AUC value of 0.941, which demonstrating higher predictive performance. The interpretability of LRC-DBN reveals that the development of loess landslides is more likely in areas with elevations ranging from 894.7 m to 998.5 m, slopes ranging from 49.6° to 64°, terrain undulations spanning 31.7 m to 91.0 m, rock type T₃y and terrain humidity ranging from 2.4 to 3.9. The result provides invaluable insights for local landslide prevention endeavors and can assist decision-making in urban planning.

Keywords:

• Landslide susceptibility
• deep learning
• conditional random field
• interpretability analysis

Data availability

Data sharing is not applicable to the article no datasets were generated or analyzed during the current study.

Disclosure statement

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 article.

Additional information

Funding

This work was supported by the [National Science Foundation of Shaanxi Provence of China] under grant [number 2023-JC-QN-0359, 2024JC-YBQN-0360]; [National Natural Science Foundation of China] under grant [number 42372320, 41972292]; [Innovation Capability Support Program of Shaanxi Province] under grant [number 2021TD-54]; [Key Research and Development Program of Shaanxi Province] under grant [number 2022ZDLSF06-03]; [Inner scientific research project of Shaanxi Land Engineering Construction Group] under grant [number DJNY-YB-2023-48, DJNY-2024-18, DJNY-2024-19] and [Key Research and Development Program of Shaanxi] under grant [Program No. 2024SF-YBXM-565].