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| Format: | Recurso digital |
| Language: | English |
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2025
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| Online Access: | https://doi.org/10.5281/zenodo.14760248 |
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| author | Betzy Babu Thoppil Anugrah Premachandran Annapoorna M Ashwin Mathew Zachariah Bala Susan Jacob |
| author_facet | Betzy Babu Thoppil Anugrah Premachandran Annapoorna M Ashwin Mathew Zachariah Bala Susan Jacob |
| contents | <p>This paper presents a comprehensive analysis of the transformative role of the Internet of Things (IoT) and Machine Learning (ML) in advancing landslide monitoring and prediction for enhanced disaster resilience. Landslides, a prevalent natural hazard, pose substantial risks to life, infrastructure, and socio economic<br>stability, particularly in geographically vulnerable regions. The inherent complexity of landslides, triggered by a confluence of geological, hydrological, and meteorological factors, necessitates advanced monitoring and prediction techniques to mitigate their devastating impacts. Traditional monitoring ap proaches, often<br>constrained by limited spatial coverage, data resolution, and realtime analysis capabilities, struggle to provide timely and accurate warnings. The emergence of IoT and ML offers a paradigm shift in landslide monitoring and prediction, enabling real-time data acquisition, sophisticated analysis, and proactive risk management.<br>IoT-enabled sensor networks, comprising diverse sensors strategically deployed across landslide prone areas, provide continuous data streams on critical parameters such as rainfall intensity and duration, soil moisture content, pore-water pressure, ground vibrations (microseismic activity), and slope deformation. These<br>sensors, often low-cost, low-power, and wirelessly interconnected, transmit data to edge computing devices or cloud-based platforms for real-time processing and analysis. ML algorithms, trained on historical landslide data and associated parameters, play a pivotal role in deciphering complex patterns and anomalies within these<br>large datasets. The sources demonstrate the effectiveness of various ML models, including Random Forest, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Networks (CNN), in landslide susceptibility mapping, hazard assessment, and early warning system development.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14760248 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Advanced Sensor-Based Landslide and Earthquake Detection and Alert System Utilizing Machine Learning and Computer Vision Technologies Betzy Babu Thoppil Anugrah Premachandran Annapoorna M Ashwin Mathew Zachariah Bala Susan Jacob <p>This paper presents a comprehensive analysis of the transformative role of the Internet of Things (IoT) and Machine Learning (ML) in advancing landslide monitoring and prediction for enhanced disaster resilience. Landslides, a prevalent natural hazard, pose substantial risks to life, infrastructure, and socio economic<br>stability, particularly in geographically vulnerable regions. The inherent complexity of landslides, triggered by a confluence of geological, hydrological, and meteorological factors, necessitates advanced monitoring and prediction techniques to mitigate their devastating impacts. Traditional monitoring ap proaches, often<br>constrained by limited spatial coverage, data resolution, and realtime analysis capabilities, struggle to provide timely and accurate warnings. The emergence of IoT and ML offers a paradigm shift in landslide monitoring and prediction, enabling real-time data acquisition, sophisticated analysis, and proactive risk management.<br>IoT-enabled sensor networks, comprising diverse sensors strategically deployed across landslide prone areas, provide continuous data streams on critical parameters such as rainfall intensity and duration, soil moisture content, pore-water pressure, ground vibrations (microseismic activity), and slope deformation. These<br>sensors, often low-cost, low-power, and wirelessly interconnected, transmit data to edge computing devices or cloud-based platforms for real-time processing and analysis. ML algorithms, trained on historical landslide data and associated parameters, play a pivotal role in deciphering complex patterns and anomalies within these<br>large datasets. The sources demonstrate the effectiveness of various ML models, including Random Forest, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Networks (CNN), in landslide susceptibility mapping, hazard assessment, and early warning system development.</p> |
| title | Advanced Sensor-Based Landslide and Earthquake Detection and Alert System Utilizing Machine Learning and Computer Vision Technologies |
| url | https://doi.org/10.5281/zenodo.14760248 |