IoT-driven Landslide And Rockfall Hazards Detection And Early Warning SystemID: 2188 Abstract :This Project Presents A Comprehensive IoT-driven Landslide And Rockfall Hazards Detection And Early Warning System Specifically Designed For Monitoring Underground Mining Environments. The System Utilizes The ESP32 Microcontroller In Conjunction With The Blynk IoT Platform And A Network Of Environmental And Geotechnical Sensors To Continuously Assess Hazardous Conditions In Real Time. To Monitor Ground Saturation And Water Ingress—critical Factors In Underground Slope Stability Two Soil Moisture Sensors Are Strategically Deployed To Detect Subsurface Water Accumulation. An Additional Soil Moisture Sensor Is Configured As A Rain Gauge Substitute For Detecting Water Seepage Near Tunnel Ceilings Or Shaft Entries. To Further Enhance Hazard Detection, The System Incorporates MEMS Accelerometers, Which Continuously Monitor Micro-vibrations And Abnormal Ground Movements That May Precede Landslides Or Rockfalls. The BMP180 Sensor Collects Environmental Parameters Such As Temperature, Atmospheric Pressure, And Altitude, Which Help Identify Sudden Environmental Shifts And Ventilation Anomalies Within The Mine. All Sensor Readings Are Transmitted In Real-time To The Blynk IoT Platform Via The ESP32’s Built-in Wi-Fi Capabilities, Allowing Remote Access From Control Rooms Or Mobile Devices. In Case Of Abnormal Readings That Exceed Safety Thresholds, The System Triggers Immediate On-site Alarms Using A Buzzer And LED Indicators, While Also Sending Real-time Alerts Via Email And Push Notifications Through The Blynk App. This Cost-effective And Modular System Offers A Scalable Solution For Early Warning And Situational Awareness In Hazard-prone Underground Mining Operations, Enabling Faster Response Times And Improved Worker Safety Through Continuous And Remote Monitoring Of Potential Landslide And Rockfall Threats. |
Published:24-3-2026 Issue:Vol. 26 No. 3 (2026) Page Nos:676-683 Section:Articles License:This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. How to CiteG. Vijaya, M. Vikas, K. Rithwik Reddy, P. Naveen, K. Sandeep, M. Lokesh, IoT-driven Landslide and Rockfall Hazards Detection and Early Warning System , 2026, International Journal of Engineering Sciences and Advanced Technology, 26(3), Page 676-683, ISSN No: 2250-3676. |