Image Processing-Based Flood Detection

Ariawan, Angga; Pebrianti, Dwi; Ronny,; Akbar, Yudha Maulana; Margatama, Lestari; Bayuaji, Luhur

doi:10.1007/978-981-13-3708-6_32

Cited by 2 publications

(1 citation statement)

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“…Image processing of webcam images utilizing Raspberry-Pi and optical sensors is handy for flood detection [20]. Image processing algorithms are convenient for flood mapping as they can analyze terrestrial features and satellite images [21,22]. Real-time flood monitoring through the investigation of river hydromorphological features has become convenient due to video processing [23] and image processing [24] of data captured using UAVs.…”

Section: Introductionmentioning

confidence: 99%

Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia

et al. 2021

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The impact of floods is the most severe among the natural calamities occurring in Malaysia. The knock of floods is consistent and annually forces thousands of Malaysians to relocate. The lack of information from the Ministry of Environment and Water, Malaysia is the foremost obstacle in upgrading the flood mapping. With the expeditious evolution of computer techniques, processing of satellite and unmanned aerial vehicle (UAV) images for river hydromorphological feature detection and flood management have gathered pace in the last two decades. Different image processing algorithms—structure from motion (SfM), multi-view stereo (MVS), gradient vector flow (GVF) snake algorithm, etc.—and artificial neural networks are implemented for the monitoring and classification of river features. This paper presents the application of the k-means algorithm along with image thresholding to quantify variation in river surface flow areas and vegetation growth along Kerian River, Malaysia. The river characteristic recognition directly or indirectly assists in studying river behavior and flood monitoring. Dice similarity coefficient and Jaccard index are numerated between thresholded images that are clustered using the k-means algorithm and manually segmented images. Based on quantitative evaluation, a dice similarity coefficient and Jaccard index of up to 97.86% and 94.36% were yielded for flow area and vegetation calculation. Thus, the present technique is functional in evaluating river characteristics with reduced errors. With minimum errors, the present technique can be utilized for quantifying agricultural areas and urban areas around the river basin.

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