A Comparative Study of Machine Learning Algorithms for Flood Risk Prediction

Abstract

Floods are one of the most frequent and devastating natural disasters, significantly affecting human life, infrastructure, and the environment. Accurate and timely flood prediction is essential for disaster preparedness and mitigation. This study presents an intelligent flood risk prediction framework using machine learning and deep learning techniques applied to four Indian cities—Pune, Nashik, Kolhapur, and Satara—each associated with major rivers such as the Mula-Mutha, Godavari, Panchganga, and Krishna, respectively. The dataset incorporates a comprehensive set of hydrometeorological and environmental features including rainfall, temperature, humidity, wind speed, water level, discharge, groundwater level, soil moisture, atmospheric pressure, evaporation rate, and historical flood events. Four algorithms—Random Forest, Support Vector Machine (SVM), XG-Boost, and Artificial Neural Networks (ANN)—were trained and evaluated to predict the flood_risk level. The model performances were compared using accuracy, precision, recall, F1-score, and ROC-AUC. The results demonstrate that the integration of multiple data sources and ensemble techniques significantly improves predictive performance. This research contributes to the development of smart, data-driven flood early warning systems tailored for regional hydrological conditions.

Introduction

Context:

• Floods cause severe human and economic losses, especially in Maharashtra cities (Pune, Nashik, Kolhapur, Satara) vulnerable due to climate change, erratic monsoons, and rapid urbanization.

• Traditional flood forecasting methods lack accuracy, spatial detail, and adaptability.

• Machine Learning (ML) and Deep Learning (DL) offer improved predictive accuracy by analyzing large, diverse environmental datasets in real-time.

Problem:

• Existing systems fail to provide timely, accurate flood risk predictions for Maharashtra’s flood-prone cities.

• The study aims to develop a scalable ML-based flood risk prediction system to classify flood risk levels dynamically using environmental data.

Objectives:

1. Collect multi-city environmental data: rainfall, temperature, humidity, wind speed, river and groundwater levels, soil moisture, pressure, evaporation, and past floods.

2. Preprocess data by cleaning, normalizing, and encoding.

3. Build and train four ML models: Random Forest, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) to classify flood risk.

4. Evaluate models using accuracy, precision, recall, F1-score, and ROC-AUC metrics.

5. Deploy the best model on a Flask-based web interface for real-time predictions and visualization, supporting disaster management and public awareness.

Methodology:

• Data collected from four major rivers and cities: Mula-Mutha (Pune), Godavari (Nashik), Panchganga (Kolhapur), Krishna (Satara).

• Data preprocessing included missing value imputation, outlier treatment, normalization, and categorical encoding.

• Models trained and hyperparameters tuned via grid search and cross-validation.

• Model performance assessed on unseen test data.

• Web deployment enables users to input live or city-specific data and receive flood risk predictions with dynamic visualizations.

Key Points:

• Random Forest, SVM, XGBoost, and ANN were compared for flood risk classification.

• Emphasis on reducing false negatives to improve early warnings.

• Interactive dashboard designed for municipal authorities, disaster teams, and citizens.

• Framework is scalable and adaptable for other regions with new data.

This study demonstrates how ML can enhance flood forecasting accuracy and real-time risk communication in flood-prone urban areas of Maharashtra.

Conclusion

The study presents the successful development of an AI-based flood risk prediction system using machine learning and deep learning models. Utilizing a rich dataset of environmental and hydrological parameters—such as rainfall, temperature, humidity, river discharge, and groundwater level—the system effectively classifies flood risk levels across multiple cities. Among the models tested (Random Forest, SVM, XG-Boost, and ANN), the Artificial Neural Network (ANN) achieved the highest accuracy, demonstrating its strength in capturing complex nonlinear patterns. Model performance was validated using metrics like accuracy, classification reports, and confusion matrices. The system also incorporated time series analysis to uncover water level trends, offering deeper insights into flood dynamics. A user-friendly GUI was developed to enable real-time interaction with model outputs, enhancing accessibility and practical application. This intelligent, data-driven approach holds significant promise for improving early warning systems and supporting disaster mitigation efforts in both urban and rural settings.

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Copyright

Copyright © 2025 Dr. D. P. Gaikwad, Onkar Sonawane, Sujit Walunj, Vikas Lawand, Pranav Nirwane. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.