Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

Earth and Planetary Sciences

• Ability 33%
• Accuracy 83%
• Accuracy Assessment 16%
• Algorithms 33%
• Area 100%
• Average 16%
• Class 33%
• Classification 50%
• Classifier 100%
• Comparison 16%
• Datum 33%
• Detection 50%
• Difference 50%
• Discriminant Analysis 66%
• Event 50%
• Flood 66%
• Imagery 66%
• Improvement 16%
• Information 16%
• Investigation 100%
• Landsat 33%
• Landsat Thematic Mapper 33%
• Machine Learning 66%
• Map 33%
• Mediterranean Region 16%
• Model 16%
• Pixel 16%
• Standard 16%
• Support Vector Machine 66%
• Utilization 16%
• Water 50%