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@article{197953,
        author = {Varanasi Gangothri and Gunta Sneha and Attada Rajesh and Rangoyi Nithin Kumar and Muddada Bala Krishna},
        title = {IOT- Driven Disaster Forecasting Response System By Using Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12100-12104},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197953},
        abstract = {The IoT-Driven Disaster Forecasting and Response System uses built-in sensors and machine learning to accurately predict environmental dangers and send out alerts when they are needed. An Arduino Mega 2560 collects real- time environmental data from several sensors, such as the DHT11 for humidity and temperature, the BMP180 for atmospheric pressure, a rain drop sensor for detecting precipitation, and an ultrasonic sensor for measuring water level. A Python-based platform receives this data and uses a Random Forest machine learning algorithm to look at the sensor patterns and figure out how likely it is that disasters like floods, storms, or extreme weather will happen. The system automatically turns on the right alert systems based on the prediction results. For example, colored LEDs show safety, caution, or danger levels; the buzzer sounds when conditions are high-risk; and the GSM module sends emergency SMS messages to the right people. An LCD screen shows real-time updates on sensor readings and predicted status. This prototype uses Random Forest-based prediction and automated alert activation to provide a reliable early- warning system that makes communities better prepared for disasters and more efficient at responding to them.},
        keywords = {Arduino Mega, IoT, Real-Time Data, Random Forest.},
        month = {April},
        }