Review Paper On Disaster Prediction and Monitoring System Using Machine Learning Algorithms

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• Create Date 3 July, 2025
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Authors : Vidya Pardeshi, Dr. H. T. Ingale ,  Dr. Anilkumar Vishwakarma, Dr. I.S Jadhav , Dr. V.  D Chaudhari

DOI : 10.46335/IJIES.2025.10.7.18

Abstract – Natural disasters like floods, earthquakes, and landslides can cause massive damage and loss of life. Many traditional disaster management systems focus on responding after a disaster happens, rather than predicting it in advance. To solve this problem, we have developed a smart disaster prediction and monitoring system that uses IoT sensors, cloud computing, and machine learning to provide early warnings. Our system works by collecting real-time data from different sensors, such as temperature, humidity, water level, ground vibrations, and soil moisture. This data is sent to a cloud platform called Thing Speak, where it is stored and analysed. We use machine learning algorithms to study patterns in the data and predict possible disasters before they happen. If a high-risk situation is detected, the system automatically sends alerts via SMS, email, or a mobile app to authorities and people in the affected areas, allowing them to take precautions. This system is affordable, efficient, and scalable, making it useful for disaster-prone areas. It helps reduce loss of life and property by giving people enough time to prepare. In the future, we can improve this system by adding artificial intelligence for better accuracy, real-time processing using edge computing, and block chain technology for data security.