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Related Experiment Video

Updated: Sep 21, 2025

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
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Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling

Pravin R Kshirsagar1, Hariprasath Manoharan2, Samir Kasim3

  • 1Department of Artificial Intelligence, G.H Raisoni College of Engineering, Nagpur, India.

Computational Intelligence and Neuroscience
|June 3, 2022
PubMed
Summary

Wireless Sensor Networks (WSN) and artificial intelligence (AI) enable real-time landslide monitoring. This system tracks factors like precipitation and motion, providing timely alerts for hazardous situations.

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Area of Science:

  • Geosciences and Environmental Monitoring
  • Computer Science and Artificial Intelligence

Background:

  • Large-scale in-house monitoring systems are enhanced by wireless network sensor technologies.
  • Wireless Sensor Networks (WSN) are crucial for monitoring environmental variables like temperature, sound, and pressure.
  • Existing land surveillance methods struggle with real-time data analysis for catastrophic events.

Purpose of the Study:

  • To illustrate the effectiveness of Wireless Sensor Networks (WSN) combined with artificial intelligence (AI) for real-time landslide monitoring.
  • To develop an alerting system for informing populations about impending landslide risks.

Main Methods:

  • Utilized Wireless Sensor Networks (WSN) to monitor key landslide causative factors including precipitation, Earth moisture, pore-water-pressure (PWP), and motion in real-time.
  • Integrated artificial intelligence (AI) algorithms, specifically Logistic Regression, for data analysis and prediction.
  • Developed a system for remote management and real-time data interpretation.

Main Results:

  • Demonstrated the capability of WSN and AI to effectively monitor landslide-inducing factors in real-time.
  • The proposed system successfully processes environmental data to identify potential landslide risks.
  • Real-time monitoring facilitates timely detection and analysis of landslide-prone areas.

Conclusions:

  • WSN and AI integration provides a robust solution for real-time landslide monitoring and forecasting.
  • The developed alerting system can proactively inform communities about dangerous landslide situations.
  • This approach enhances land life surveillance and mitigates risks associated with catastrophic events.