Riverbank Monitoring using Image Processing for Early Flood Warning System via IoT

• Published in: International Journal of Integrated Engineering
• Main Author: Soh Z.H.C.; Razak M.S.A.; Hamzah I.H.; Zainol M.N.; Sulaiman S.N.; Yahaya S.Z.; Abdullah S.A.C.
• Format: Article
• Language: English
• Published: Penerbit UTHM 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132768275&doi=10.30880%2fijie.2022.14.03.018&partnerID=40&md5=b228b1fcd1b8945af9b4ab83aaee85cd

This paper is presented a system that monitor the river water level by using computer vision with image processing and IoT. This system is developed to detect riverbank level and river water level by applying image processing where edge detection technique is applied on both images captured by video camera. The flood severity level is determined by comparing the river water level and the riverbank level. Then, the determined flood severity is upload to IoT platform. A notification is sent the people when the flood severity level reached certain critical level via Telegram app which one of the social media applications. The available Raspberry Pi 3 Model B is used as a controller in this system hardware device with the Raspberry Pi 5MP camera module. The IoT platform used is Ubidots where the user can be notified through it. The main contribution of this work is on the integration of computer vision with IoT Cloud as an early flood monitoring system in responding to climate change by determining the flood severity level and alert the community on the flood severity condition. Experimental results shown that it is viable approach to combine computer vision with an artificial intelligent image processing and the IoT Cloud platform. The work makes a comparison of the Canny Edge Detection technique and the threshold technique for determining the water level and the river bank level. This system also had been tested in lab (indoor) environment and outdoor environment to check the suitability of this system to operate at the real environment. © Universiti Tun Hussein Onn Malaysia Publisher’s Office