Paper Title
URBAN DELUGE DETECTION USING SATELLITE IMAGERY AND MACHINE LEARNING
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Registration ID: IJNRD_305202
Published ID: IJNRD2504092
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Keywords
Deluge, urban, satellite image, machine learning
Abstract
Urban flooding, also known as urban deluge, is a critical environmental challenge faced by modern cities due to rapid urbanization, poor drainage infrastructure, and increasing occurrences of extreme weather events induced by climate change. The dense concentration of buildings, roads, and impervious surfaces in urban areas prevents water from naturally percolating into the ground, causing water to accumulate quickly during heavy rainfall. Such urban floods result in severe damage to property, disruption of transportation and communication systems, and loss of human lives. The need for a reliable and efficient system to detect and monitor urban deluge in real-time is crucial for minimizing socio-economic impacts and aiding disaster management authorities in their mitigation efforts. This project presents an intelligent urban deluge detection system that utilizes satellite imagery and machine learning techniques for accurate and timely identification of flood-affected areas in urban environments. High-resolution satellite images, including multispectral land synthetic aperture radar(SAR) data, are collected to observe ground conditions before, during, and after heavy rainfall events. These images provide critical information such as changes in water bodies, surface reflectance, and terrain elevation, which are vital indicators of flooding. The collected satellite data is pre-processed to enhance image quality, remove noise, and extract relevant features. Important attributes such as water coverage, impervious surfaces, vegetation, and elevation are derived using image processing techniques. These features are then fed into machine learning models, particularly Convolutional Neural Networks (CNNs),which are well-suited for image classification tasks due to their ability to automatically learn spatial hierarchies of features from raw data. The CNN model is trained using labeled data sets containing images of both flooded and non-flooded urban areas to learn patterns and accurately classify affected regions. Additionally, the model incorporates historical flood data and rainfall patterns to improve its prediction capabilities. By combining real-time satellite imagery with past data, the system can better assess flood risks and detect flooded zones with high precision. The use of SAR imagery also ensures reliable detection even in cloudy or rainy conditions where optical imagery may fail. The proposed urban deluge detection system offers several advantages. It provides a scalable and efficient solution for large-area monitoring, reduces the need for manual ground surveys, and delivers rapid assessments crucial for emergency response. The output of the system can be visualized on geographic information system (GIS) platforms, providing authorities with actionable insights for rescue operations, resource allocation, and flood management planning.
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How To Cite (APA)
D.Sowmya, R.Jagan, & Rakeshkumar Maheshbhai Makwana (April-2025). URBAN DELUGE DETECTION USING SATELLITE IMAGERY AND MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), a798-a808. https://ijnrd.org/papers/IJNRD2504092.pdf
Issue
Volume 10 Issue 4, April-2025
Pages : a798-a808
Other Publication Details
Paper Reg. ID: IJNRD_305202
Published Paper Id: IJNRD2504092
Research Area: Science and Technology
Author Type: Indian Author
Country: Salem, Tamil nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2504092.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2504092
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