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This groundbreaking work by Dr. Siddique presents cutting-edge methodologies for flood monitoring and prediction using satellite images. Drawing from extensive research conducted in Uttar Pradesh, India, the book offers a comprehensive framework for developing Flood Monitoring and Early Warning Systems (FMEWS) through advanced image processing and machine learning techniques. Readers will master the implementation of various classification methods, time-series analysis, and change detection techniques using Sentinel-1 satellite images, enabling them to identify flood-prone areas and predict…mehr

Produktbeschreibung
This groundbreaking work by Dr. Siddique presents cutting-edge methodologies for flood monitoring and prediction using satellite images. Drawing from extensive research conducted in Uttar Pradesh, India, the book offers a comprehensive framework for developing Flood Monitoring and Early Warning Systems (FMEWS) through advanced image processing and machine learning techniques. Readers will master the implementation of various classification methods, time-series analysis, and change detection techniques using Sentinel-1 satellite images, enabling them to identify flood-prone areas and predict flood patterns with unprecedented accuracy. The book bridges critical research gaps by introducing innovative approaches to flood assessment, including Random Forest and KNN-based classification, and deep learning models. Whether you're a researcher, practitioner, or student in computer science or environmental sciences, this practical guide will equip you with the tools and knowledge needed to develop robust flood monitoring systems that can help save lives and protect communities.
Autorenporträt
Dr. Mohammed Siddique, a passionate researcher with an extensive research on Satellite Image Processing, Machine Learning and Deep Learning. He holds a PhD in Computer Applications from Integral University, India. He has a wide-ranging academic and industrial experience in computer science and has received research award for his distinguished work.