Habitat quality is a comprehensive index reflecting ecological conditions, land use impact, and human survival. Susceptibility to geological disasters is influenced by factors such as ecology, the geological environment, and human activities. Analyzing the effects of habitat quality on geological disaster susceptibility and its spatial dynamics is crucial for ecological protection and assessing geological disaster risks. This research focused on Pingshan County, using the InVEST 3.7.0 model and ArcGIS to evaluate habitat quality and geological disaster susceptibility for 2020. The spatial relationships were examined with GeoDa to investigate the impact of habitat quality on geological disaster susceptibility. The findings are as follows: (1) Pingshan County generally exhibits high habitat quality, showing significant spatial clustering with geological disaster susceptibility—predominantly high–high in the west and low–low in the east. (2) The geological environment significantly influences the relationship between habitat quality and geological disaster susceptibility, with an overall positive correlation but negative correlations in certain areas. Geological disaster susceptibility is primarily governed by geological factors rather than habitat quality. (3) In mountainous regions with comparable ecological and geological conditions, variations in geological disaster susceptibility are chiefly driven by human activities. Including human activities as a metric significantly enhances the evaluation accuracy. This study provides a scientific foundation for ecological protection, the assessment of geological disaster susceptibility, and the development of mitigation policies.

Accurate prediction of the risk levels of debris flows is crucial for devising effective disaster prevention and mitigation strategies. This study, based on debris flow sample data from Yunnan Province, initially employs Principal Component Analysis to reduce the dimensionality of the raw data, extracting key features and minimizing data dimensions. Subsequently, a 5-fold cross-validation method is utilized to segment the dataset into training and testing sets, and a predictive model integrating Principal Component Analysis with an Elman Neural Network (PCA-Elman) is constructed. The study investigates the impact of data imbalance and spatial variability on the model’s predictive accuracy and attempts to enhance the model’s generalization capabilities by employing the Adaptive Synthetic Sampling algorithm and incorporating samples from unknown regions. The results indicate that the model demonstrates high accuracy and generalization in predicting debris flow risks, with its Area Under Curve value, Average Precision value, and average precision scores surpassing those of traditional models, achieving an accuracy rate of 88.57%. After processing the data with the Adaptive Synthetic Sampling algorithm, the model’s accuracy rate increases to 98.33%. Furthermore, incorporating samples from unknown regions into the trained model significantly improves the prediction accuracy for debris flow risks in those areas. This research offers new insights into the assessment of debris flow hazards and disaster prevention and mitigation efforts, and provides a reference for the construction of predictive models for similar natural disasters.