Real-Time Flood Alert System in Parking Lot Using IoT and Machine Learning

Urban regions, whether coastal or inland, are more susceptible to flash floods owing to insufficient drainage systems and erratic weather patterns. Parking lots in low-lying areas are particularly susceptible, often leading to car submersion and considerable property damage. This study introduces a real-time flood alert system that uses the Internet of Things (IoT) and Machine Learning (ML) technologies to monitor and mitigate flood threats in parking lots. The proposed system incorporates water level sensors for precise flood detection and slot sensors to identify and manage available parking spots. A real-time video feed, sent via a cloud platform, allows remote visual surveillance of flood conditions. The system automatically allocates parking spaces and notifies users when elevated water levels endanger their automobiles. It uses an Arduino-based controller and GSM modules for efficient data processing and transmission. ML algorithms analyze sensor data, forecast flood conditions, and initiate prompt warnings via SMS or online notifications. The system has undergone experimental validation in controlled settings, showcasing its efficacy in early flood detection, real-time monitoring, and intelligent parking management. It provides a scalable, costeffective solution for urban flood resistance and infrastructure protection.