International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Multi Source Machine Learning Framework for Intelligent Flood Prediction and Early Warning |
|---|---|
| ABSTRACT | Floods are one of the most destructive natural disasters, causing severe damage to human life, property, and the environment. This paper presents a Machine Learning-Based Flood Disaster Prediction and Management System for predicting flood occurrence using environmental parameters. The system utilizes Random Forest, Support Vector Machine (SVM), and Logistic Regression algorithms to analyze rainfall, river level, temperature, humidity, wind speed, and drainage conditions. The application is developed using Python and the Flask framework to provide an interactive web-based platform. It also includes disaster alert notifications and relief center information to support emergency response. The Random Forest model provides better prediction performance compared to the other implemented models. The proposed system improves early flood warning, disaster preparedness, and decision-making. Overall, it offers an efficient, reliable, and user-friendly solution for flood disaster prediction and management. |
| AUTHOR | G. REVATHI, B. DURGA BAAVANI Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407060 |
| pdf/60_A Multi Source Machine Learning Framework for Intelligent Flood Prediction and Early Warning.pdf | |
| KEYWORDS | |
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