Image processing-based flood detection

Ariawan, Angga and Pebrianti, Dwi and Ronny and Akbar, Yudha Maulana and Margatama, Lestari and Bayuaji, Luhur (2019) Image processing-based flood detection. In: Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 27-28 September 2018 , Universiti Malaysia Pahang. pp. 371-380., 538. ISBN 978-981-13-3708-6 (Online)

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This paper discusses about the design of an online ftood detection and early warning system which integrated to using Raspberry-PI and optical sensor. Raspberry-PI is a single board of computer which in this case we design as an image processor to process image obtained from the webcam and update the result to the twitter. This research can help some of the citizens who live near the river to get tbe updated information regarding water conditions and the possibility of flooding so that they can take action to secure their properties and families as soon as possible. We use OpenCV as an image processing application. The steps are as follows: (1) Region of Interest to create a portion of an image to filter or perform some other operation. (2) Brightness and contrast adjustment in order to get brighter and better image before the next process. (3) Grayscale and threshold to create segmentation object with Otsu-thresholding. ( 4) Edge detection algorithm to find edge points on a roughly horizontal water line and riverbank height By using the above method, the system can read and monitor the \Valer level of a river or other water bodies. If the water level exceeds the specific threshold, the system will generate notification as early warning for the possibility of floodi ng by uploading the text and image to the twitter regarding that condition. The citizens will get the information if they follow that account (early warning system) on Twitter. The result of this simulation using prototype that we have made is that the system can read the water conditions with an increase in accuracy reaching 99.6o/o.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Part of the Lecture Notes in Electrical Engineering book series
Uncontrolled Keywords: Flood detection; Early warning system; Optical sensor; Image processing
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Faculty of Electrical & Electronic Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 09 Dec 2019 03:33
Last Modified: 09 Dec 2019 03:33
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