Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design
IntroductionThe increasing frequency of slope disasters in urban and recreational public spaces, driven by climate change, presents significant risks to public safety and sustainable urban design. Conventional slope stability monitoring systems rely heavily on static models and manual interventions,...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1536481/full |
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| author | Wang Ting Ying Wang |
| author_facet | Wang Ting Ying Wang |
| author_sort | Wang Ting |
| collection | DOAJ |
| description | IntroductionThe increasing frequency of slope disasters in urban and recreational public spaces, driven by climate change, presents significant risks to public safety and sustainable urban design. Conventional slope stability monitoring systems rely heavily on static models and manual interventions, often lacking adaptability and real-time predictive capacity. Earlier methods, including rule-based and empirical approaches, use fixed thresholds to assess risk factors such as soil moisture, slope angle, and seismic activity. Although machine learning models like decision trees and support vector machines have improved predictions using historical data, their scalability and adaptability remain constrained due to the need for intensive feature engineering and their limited ability to model complex nonlinear dynamics.MethodsThis study introduces a novel framework utilizing Deep Learning techniques to enable intelligent, real-time monitoring and early warning of slope disasters. The Adaptive Spatial Design Model (ASDM) incorporates real-time geospatial data, user behavior analytics, and environmental sensing to dynamically assess risk. It employs convolutional and recurrent neural networks for geo-hazard prediction, graph-theoretic optimization for decision-making, and adaptive spatial strategies to enhance model accuracy and responsiveness in changing environments.ResultsExperimental validation on real-world datasets shows that the proposed system effectively reduces false alarms and improves response times by 35% compared to traditional methods. The integration of neural network-based prediction with adaptive spatial planning enhances both the precision and timeliness of disaster warnings.DiscussionThis framework offers a transformative, safe, and functional approach to slope disaster management in dynamic public spaces. It advances sustainability and resilience by optimizing spatial design and human-environment interactions. The model's adaptability to environmental changes represents a significant improvement in urban design and disaster mitigation strategies. |
| format | Article |
| id | doaj-art-2e0c19587f6c46d7b1a50f447c0784eb |
| institution | Kabale University |
| issn | 2296-665X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj-art-2e0c19587f6c46d7b1a50f447c0784eb2025-08-20T03:52:46ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2025-05-011310.3389/fenvs.2025.15364811536481Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space designWang Ting0Ying Wang1School of Art, Anhui Xinhua University, Hefei, Anhui, ChinaAnhui Urban Management Vocational College, Hefei, ChinaIntroductionThe increasing frequency of slope disasters in urban and recreational public spaces, driven by climate change, presents significant risks to public safety and sustainable urban design. Conventional slope stability monitoring systems rely heavily on static models and manual interventions, often lacking adaptability and real-time predictive capacity. Earlier methods, including rule-based and empirical approaches, use fixed thresholds to assess risk factors such as soil moisture, slope angle, and seismic activity. Although machine learning models like decision trees and support vector machines have improved predictions using historical data, their scalability and adaptability remain constrained due to the need for intensive feature engineering and their limited ability to model complex nonlinear dynamics.MethodsThis study introduces a novel framework utilizing Deep Learning techniques to enable intelligent, real-time monitoring and early warning of slope disasters. The Adaptive Spatial Design Model (ASDM) incorporates real-time geospatial data, user behavior analytics, and environmental sensing to dynamically assess risk. It employs convolutional and recurrent neural networks for geo-hazard prediction, graph-theoretic optimization for decision-making, and adaptive spatial strategies to enhance model accuracy and responsiveness in changing environments.ResultsExperimental validation on real-world datasets shows that the proposed system effectively reduces false alarms and improves response times by 35% compared to traditional methods. The integration of neural network-based prediction with adaptive spatial planning enhances both the precision and timeliness of disaster warnings.DiscussionThis framework offers a transformative, safe, and functional approach to slope disaster management in dynamic public spaces. It advances sustainability and resilience by optimizing spatial design and human-environment interactions. The model's adaptability to environmental changes represents a significant improvement in urban design and disaster mitigation strategies.https://www.frontiersin.org/articles/10.3389/fenvs.2025.1536481/fullslope disastersadaptive designdeep learningreal-time monitoringpublic space design |
| spellingShingle | Wang Ting Ying Wang Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design Frontiers in Environmental Science slope disasters adaptive design deep learning real-time monitoring public space design |
| title | Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design |
| title_full | Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design |
| title_fullStr | Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design |
| title_full_unstemmed | Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design |
| title_short | Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design |
| title_sort | utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design |
| topic | slope disasters adaptive design deep learning real-time monitoring public space design |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2025.1536481/full |
| work_keys_str_mv | AT wangting utilizingdeeplearningforintelligentmonitoringandearlywarningofslopedisastersinpublicspacedesign AT yingwang utilizingdeeplearningforintelligentmonitoringandearlywarningofslopedisastersinpublicspacedesign |