Vol.13, No.2, May 2024.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Smart Agriculture System of Flood Monitoring and Mitigation Using Live Data for Flood-Prone Area


Sangtong Boonying, Parinya Natho, Suwit Somsuphaprungyos, Salinun Boonmee


© 2024 Salinun Boonmee, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 13, Issue 2, Pages 1571-1579, ISSN 2217-8309, DOI: 10.18421/TEM132-70, May 2024.


Received: 22 December 2023.

Revised:   02 March 2024.
Accepted: 12 March 2024.
Published: 28 May 2024.




This research proposes a solution to improve the system for monitoring relevant environmental parameters using sensors for flood mitigation. Sensors are used to collect data regarding farm flood situation. The collected data are trained for a classification model to activate the solar-powered water pump to mitigate flood incidents in a flood-prone area. The system helps farmers to monitor real-time environmental parameters relevant to farming operations and flood including soil moisture level, water level, and water flow speed in a nearby canal that provides water to the farm. To reduce flood damage, the system assists to drain the excessive water to prevent prolonged submerging of the crop. The devices are designed to use the electricity from solar power, so the system is practically used outdoor where an electricity cord is difficult to setup. Experimental results show that the sensing data from the deployed sensors are accurate. The generated prediction models give the high performance with average of 1.0, 0.97, and 0.93 F-1 score for no-flooding, mild-flooding, and severe flooding respectively.


Keywords – Smart agriculture system, intelligent pumps, flood monitoring system, IoT technology.



Full text PDF >  



Copyright © 2024 UIKTEN
Copyright licence: All articles are licenced via Creative Commons CC BY-NC-ND 4.0 licence