Sensor Data-Powered Disaster Detection with ML Optimized Risk Assesment

Landslides in mountains regions pose significant risks to human life, infrastructure and the environment, making effective monitoring and predication essential. This paper proposes an advanced landslide mitigation system that integrates IOT sensors and Machine Learning (ML) algorithms to predictlandslides and provide early warnings, thereby enhancing community safety and disaster preparedness. The System continuously monitors critical parameters such as soil moisture, rainfall intensity, and slope stability, which are vital indicators of landslide risk. By employing a pre-defined ML algorithm, the system analyses the real time sensor data, allowing it to predict landslide occurrences up to 24 hours in advance. This timely prediction enables authorities and residents to take proactive measures, reducing potential casualties and damage. To effectively communicate risk levels, the system features a three – level alarm mechanism: Green (safe) indicates stable condition, Yellow (potential risk) signals the need for caution, and Red(imminent danger) wants of an impending landslide, promoting immediate action. Moreover, the system includes a user -friendly interface that residents to access real-time weather updates and landslide risk assessments easily. The interface is designed for ease of use, ensuring that critical information isreadily available to those who need it most. Ultimately, this advanced landslide mitigation system aims to reduce the impact of landslides through timely alerts, fostering a culture of preparedness and communities. By integrating innovative technologies and community involvement, we aspire to protect lives and safeguard infrastructure in high-risk areas.