Landslides are natural disasters that can cause significant damage to infrastructure, the environment, and human lives. Detecting landslides in a timely and accurate manner is critical for minimizing their impact. This project focuses on developing a landslide detection system using advanced technologies such as machine learning, remote sensing, and IoT. By integrating geological data, weather conditions, and real-time sensor inputs, the system aims to identify potential landslide-prone areas and provide early warnings. The proposed approach leverages predictive modelling to analyse patterns and correlations in large datasets, ensuring reliable and efficient detection. This solution can assist authorities in disaster management and help communities prepare for and mitigate the risks associated with landslides.
Landslides are natural disasters that can cause significant damage to infrastructure, the environment, and human lives. Detecting landslides in a timely and accurate manner is critical for minimizing their impact. This project focuses on developing a landslide detection system using advanced technologies such as machine learning, remote sensing, and IoT. By integrating geological data, weather conditions, and real-time sensor inputs, the system aims to identify potential landslide-prone areas and provide early warnings. The proposed approach leverages predictive modelling to analyse patterns and correlations in large datasets, ensuring reliable and efficient detection. This solution can assist authorities in disaster management and help communities prepare for and mitigate the risks associated with landslides.
This research received no external funding or commercial grants.
The authors declare that there are no competing financial or personal interests related to this work.
Not applicable. This study did not involve human participants, animal subjects, or sensitive clinical data.
All data supporting the findings of this study are included within the article and its supplementary materials.