IoT WITH AI-DRIVEN DISASTER FORECASTING AND RESPONSE SYSTEM USING NEURAL NETWORKS

Abstract: The proposed Disaster Response System is a hybrid IoT and Artificial Intelligence-based solution designed using an Arduino/ESP32 microcontroller and a laptop for advanced ML/DL processing. The system collects real-time environmental data such as soil moisture, rainfall, vibration, and water level using sensors connected to the microcontroller. This sensor data (text input) is transmitted to a laptop, where Machine Learning algorithms analyze patterns to predict disaster conditions. Additionally, Deep Learning models process image and video inputs to detect disasters such as floods, landslides, or structural damage. Based on predictions, the system generates alerts via Telegram cloud, and LCD display. This approach ensures faster response, improved accuracy, and reduced disaster impact.

Keywords: Arduino/ESP32 microcontroller, soil moisture sensor, rainfall detection sensor, Ultrasonic Sensor (Water Level) , MPU6050 (Vibration Sensor), Machine Learning, Deep Learning, Telegram cloud, LCD display