Abstract
Floods are among the most devastating natural disasters, causing significant loss of life and property. This project proposes a Smart Flood Alert System using Machine Learning (ML), IoT, and a Website for real-time monitoring and early warning generation. The system uses an ESP32 microcontroller interfaced with sensors such as water level sensor, rain sensor, temperature sensor, humidity sensor, and GPS module to collect real-time environmental data. The collected data is displayed locally on an LCD and transmitted to a cloud-based server. A machine learning model analyzes the sensor data to predict flood risks. The website provides real-time visualization, alerts, and location-based tracking. The system enables early warnings via handheld communication devices, ensuring faster response and disaster preparedness.

INTRODUCTION:
Floods are unpredictable and can occur due to excessive rainfall, overflowing rivers, or poor drainage systems. Traditional flood monitoring systems are often manual, slow, and unreliable. With advancements in IoT and machine learning, it is possible to design intelligent systems capable of real-time monitoring and prediction. This project integrates sensors, ESP32, ML algorithms, and a web platform to create a smart flood alert system. The system continuously monitors environmental parameters, predicts flood risks, and sends early warnings to authorities and users.

Objectives
1. To continuously monitor water levels, rainfall, and weather conditions in flood-prone areas using low-cost IoT sensors.
2. To integrate GPS modules for precise location tracking of sensor nodes and enable geofenced, location-specific flood alerts.
3. To process data locally using edge computing, ensuring quick detection of flood risks even when internet connectivity is poor.
4. To transmit data to a cloud server using Wi-Fi.
5. To develop a machine learning model for flood prediction.
6. To design a website for real-time visualization and alerts.
7. To provide location-based warnings using GPS.

Block Diagram