Utilizing Deep Learning for Intelligent Monitoring and Early Warning of Slope Disasters in Public Space Design

Wang Ying^*

• Anhui Vocational College of City Management, Hefei, China

The increasing frequency of slope disasters in urban and recreational public spaces, exacerbated by climate change, poses significant challenges to public safety and sustainable design. Addressing this critical issue aligns closely with themes of adaptive environmental response and sustainability, as explored in the special issue on physiological and pathological responses to hypoxia and high altitudes. Traditional monitoring systems for slope stability rely heavily on static models and manual interventions, which often lack real-time adaptability and predictive accuracy. Early approaches, such as knowledge-based rule systems and empirical models, utilized predefined thresholds for factors like soil moisture, slope angle, and seismic activity to assess risks. While interpretable, these methods struggle with scalability and adaptability in dynamically changing terrains. Machine learning-based models, including decision trees and support vector machines, improved predictive capabilities by leveraging historical data patterns, but they remain limited by the need for extensive feature engineering and their inability to capture complex nonlinear relationships in slope dynamics. Recent advances in deep learning have enabled the use of convolutional and recurrent neural networks for geohazard prediction, enhancing both spatial and temporal analysis. Existing models often fail to integrate multimodal real-time sensor data, limiting their effectiveness in dynamic public space environments. In this study, we propose a novel framework leveraging Deep Learning techniques for intelligent monitoring and early warning of slope disasters. Our Adaptive Spatial Design Model (ASDM) integrates real-time geospatial data, user behavior analytics, and environmental sensing to dynamically assess risks and provide actionable insights. By employing neural network-based predictive models and adaptive graph-theoretic optimization, the system enhances the precision and timeliness of disaster warnings, while also optimizing the spatial design for diverse user interactions. Experimental validation on real-world datasets demonstrates the model's efficacy in reducing false alarms and improving response times by 35% compared to traditional methods. These advancements ensure public spaces remain safe, functional, and adaptive to environmental challenges, embodying a transformative approach to urban design and disaster mitigation.