Landslide Alert System Using Machine Learning And Iot Sensors For Real Time Prediction And Notification

Abstract

Natural Disasters, Such As Heavy Rainfall And Landslides, Pose Significant Threats To Life And Infrastructure, Particularly In Vulnerable Regions. This Work Proposes A Hybrid Disaster Alert System That Utilizes An IoT-enabled Embedded System, Integrating Various Environmental Sensors To Predict And Alert Users About Potential Hazards. Landslides Are A Significant Natural Hazard That Can Cause Extensive Damage To Infrastructure And Loss Of Life. Timely And Accurate Prediction Of Landslides Is Crucial For Mitigating These Risks. This Work Presents An Advanced Landslide Alert System Leveraging Deep Learning Techniques And Internet Of Things (IoT) Sensors For Real-time Prediction And Notification. The System Utilizes A Recurrent Neural Network (RNN) Model Trained On Historical Landslide Data And Real-time Sensor Inputs, Including Soil Moisture, Temperature, Humidity, And Ground Vibration. These Sensors, Interfaced With An Arduino Microcontroller, Continuously Monitor The Environmental Conditions And Transmit Data To The RNN Model. The Model Processes This Data To Predict The Likelihood Of A Landslide And Triggers An Alert If The Risk Exceeds A Predefined Threshold. The System's Architecture Ensures Low Latency And High Accuracy In Predictions, Enabling Timely Evacuation And Preventive Measures. This Integrated Approach Combines The Power Of Deep Learning With IoT Technology To Provide A Robust And Reliable Landslide Early Warning System, Significantly Enhancing Disaster Preparedness And Response Capabilities.

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