Real-Time Avalanche Hazard Monitoring System Based on Weather Sensors and a Laser Rangefinder

Area of Science:

• Geosciences
• Environmental Monitoring
• Remote Sensing

Background:

• Avalanche hazard prediction is critical for mountainous regions globally.
• Existing monitoring methods often lack continuous, high-resolution data.
• The East Kazakhstan region faces significant avalanche risks.

Purpose of the Study:

• To develop and field-test a prototype automated avalanche hazard monitoring system.
• To integrate various sensors and data processing capabilities for comprehensive monitoring.
• To assess the system's effectiveness in avalanche prediction.

Main Methods:

• Deployment of a snow avalanche station with environmental sensors (temperature, humidity, pressure, wind).
• Integration of a temperature snow measuring rod and an API for data storage.
• Utilization of autonomous solar power for continuous operation.
• Application of logistic regression for avalanche prediction using collected meteorological data.

Main Results:

• The automated monitoring system demonstrated continuous, high-resolution data collection.
• Field testing identified system strengths and weaknesses.
• Avalanche prediction model achieved over 98% recognition accuracy.
• Regression coefficients derived from meteorological data proved effective for prediction.

Conclusions:

• The developed automated system shows significant promise for improving avalanche risk management.
• The system's high forecasting accuracy supports its practical application.
• The solution is scalable for broader national and international use in avalanche hazard assessment.