International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
“FLOOD PREDICTION AND MANAGEMENT USING RANDOM FOREST MACHINE LEARNING ALGORITHM” Prof. V.G.Khetade1, Prerana Phatale2, Atharv Pujari3, Deven Pujari4, Shreyas Pujari5, Omkar Sutar6 D.K.T.E Society’s Textile and Engineering Institute Department of Computer Science & Engineering ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Flooding ranks among the most devastating
Lately, the integration of Internet of Things technologies (IoT), machine learning (ML), and cloud computing has revolutionized how environmental data can be captured, analyzed, and acted upon. IoT devices such as water level sensors and weather monitoring stations provide continuous, real-time data streams from vulnerable zones. Machine learning algorithms can then process this data to identify patterns, predict future outcomes, and trigger early warnings. Cloud-based systems ensure that the data and services remain scalable, accessible, and resilient under high-load conditions.
natural calamities, often resulting in substantial damage to property and loss of human life. This paper presents an integrated IoT and machine learning-based system for realtime flood monitoring, prediction, and emergency response. The proposed system collects environmental data such as rainfall, temperature, humidity, river discharge, and water level using an IoT sensor module built with NodeMCU (ESP8266). The data is processed and sent to a cloud-hosted server for real-time analysis. A machine learning model is employed to predict the likelihood of a flood based on multiple input parameters. The results are displayed through a webbased application, which also leverages the Google Maps API to provide users with location-based emergency alerts and nearby resources like hospitals and shelters. The system includes secure user authentication, an admin panel for emergency resource management, and a user-friendly interface for public access. Experimental results confirm that the solution is accurate, scalable, and capable of assisting users during flood emergencies
This paper proposes a smart, real-time flood prediction and emergency management system that combines the capabilities of IoT sensing, machine learning-based prediction, and interactive web technologies. The system's architecture is composed of a NodeMCU (ESP8266) microcontroller to collect live water level data, while rainfall, temperature, and humidity data are sourced from the OpenWeather API. The remaining inputs, such as river discharge and elevation, are either retrieved from authoritative sources or manually entered by users for precise prediction.
Key Words: Flood prediction, IoT, NodeMCU ESP8266, machine learning, emergency response, water level monitoring, Google Maps API
At the core of this project lies a Random Forest Classifier trained on historical and real-time environmental data to forecast the occurrence of flood events. A Flask-based backend processes user inputs and model predictions, while a React.js frontend presents users with an intuitive interface. If a flood is predicted, the application accesses the user’s geolocation via the browser, fetches nearby hospitals, police stations, and shelters using the Google Maps API, and displays them on an interactive map. This ensures that users not only receive an alert but also immediate access to lifesaving resources.
1.INTRODUCTION Floods are some of the most common and damaging natural hazards worldwide, endangering human lives, damaging property, affecting agriculture, and disrupting essential infrastructure. The rising occurrence and severity of floods can be attributed to factors such as climate change, unchecked urban growth, deforestation, and inadequate water resource management. Based on data from the UN Office for Disaster Risk Reduction (UNDRR), floods accounted for more than 43% of all disaster events recorded in the last two decades, affecting billions and causing trillions in economic losses.
This system has been designed with scalability, affordability, and accessibility in mind. Its modular structure allows for easy expansion and deployment in different geographical regions. The integration of open-source tools and APIs reduces the cost barrier, making it suitable for communitylevel and governmental adoption. By merging predictive analytics with real-time location intelligence, this aims to transform flood response from reactive to proactive, ensuring better preparedness and faster recovery during disasters.
Traditional flood detection and alert systems, often relying on manual observations or delayed centralized weather forecasts, are inadequate in providing timely and accurate warnings to at-risk communities. These conventional systems frequently suffer from limited geographical coverage, lack of real-time monitoring, and insufficient integration with localized emergency infrastructure.
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