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Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, Institute of Electrical and Electronics Engineers Inc..
Source : 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, Institute of Electrical and Electronics Engineers Inc. (2017)
ISBN : 9781509006113
Keywords : Artificial intelligence, Character recognition, climate change, Comparative studies, Disasters, Edge detection, Feature extraction, FIR filters, Flood hazard assessment, Flood waters, floods, Flow velocity, Gages, Global warming, image classification, Image processing, Image processing - methods, Image segmentation, Natural disasters, Physical characteristics, Processing, Region of interest, Reliable systems, Sensors
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Wireless Networks and Applications (AWNA)
Year : 2017
Abstract : Global warming induced drastic climate changes have increased the frequency of natural disasters such as flooding, worldwide. Flooding is a constant threat to humanity and reliable systems for flood monitoring and analysis need to be developed. Flood hazard assessment needs to take into account physical characteristics such as flood depth, flow velocity and the duration of flooding. This paper provides the researchers with a detailed compilation of the methods that can be used for the estimation of flood water depth. A comparative study has been done between the water depth estimation techniques based on image processing and those which does not involve image processing. The comparison is based on various attributes such as implementation methods, advantages, accuracy and cost. Image processing methods are classified based on various algorithms such as character recognition, feature extraction, region of interest (ROI), FIR filter etc. Similarly, non-image processing methods are classified based on hardware used such as sensors, level indicators, etc., and other signal based techniques. This study can be used to identify the best method for flood water depth estimation.
Cite this Research Publication :
B.B. Nair and Sethuraman Rao, “Flood water depth estimation-A survey”, in 2016 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2016, 2017.