Title: A machine learning and IoT-based novel forecasting approach for flood monitoring and prevention

Authors: Kakali Das; Sagnik Ghosh; Himadri Sekhar Dutta

Addresses: Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, India; Affiliated to: Maulana Abul Kalam Azad University, India ' Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, India ' Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, India

Abstract: The internet of things and machine learning algorithms have brought about a revolution in the field of technology as it has treated several common problems at ease. Things have become automated and easier due to the advent of these techniques. Floods have drained out civilisations and are a serious threat as ever. Internet of things along with machine learning is tried out in this paper to bridge the gap between arrival of the flood and the cautionary measures to prevent loss of life and property. In this study, a unique, advanced and a very efficient IoT-based sensor system is proposed to set the scheme of sending the notification through SMS alert whenever water level and rainfall count crosses the threshold value. The other extent of this paper is the future prediction of upcoming floods and heavy rainfall through machine learning algorithm.

Keywords: node MCU; ultrasonic sensor; water flow sensor; SMS alert; machine learning; internet of things.

DOI: 10.1504/IJHI.2023.129344

International Journal of Hybrid Intelligence, 2023 Vol.2 No.2, pp.175 - 189

Accepted: 23 Dec 2022
Published online: 06 Mar 2023 *

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