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Landslidesusceptibility mapping (LSM) is the primary link of geological disaster risk evaluation, which is significant for postearthquake emergency response and rebuilding after disasters. Existing LSM studies applying deep learning (DL) methods have shortcomings such as easy overfitting, slow convergence, and insufficient hyperparametric optimization. In response to these problems, this study proposes an ensemble model based on ant colony optimization strategy and deep belief network (ACO-DBN). In ACO-DBN, DL optimization strategies were added to DBN and their combined parameters were optimized with ACO. Next, Pearson&#x0027;s correlation coefficient and random forest importance ranking methods were utilized to optimize landslide causative factors. Then, the Jiuzhaigou earthquake region was selected as an example to explore the applicability of this model. Besides, we conducted the Wilcoxon signed rank test in order to verify that the differences were statistically significant. In a comprehensive comparative all indexes and landslide density, the model proposed in this article shows good rationality, scientificity, and interpretability. The newly occurred landslide site further demonstrates that heuristically optimized DL could make scientific and accurate evaluation of landslide susceptibility.

Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region