IDISense: IoT-Based Dam Water Disaster Sensing and Prevention System

• Published in: IEEE sensors journal 2023-12, Vol.23 (23), p.29451-29457
• Main Authors: Braveen, M., Anusha, K., Jerlin, M. Asha, Seetha, R., Sampath, Nithya
• Format: Article
• Language: English
• Subjects: Cloud computing; Control rooms; Dam; Dams; deep learning; Floods; Internet of Things; Internet of Things (IoT); Meteorology; Monitoring; Rain; Rainfall; Sensors; spiking neural network (SNN); Waterfalls; Weather
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3322290

Rapid changes in the environment and geographical conditions can cause major disasters. Monitoring these changes to save lives is a major challenge. In addition to monitoring, timely action is also important. Achieving this goal requires timely preventative information. In this article, a novel Internet of Things (IoT)-based IDISense app has been proposed to monitor and broadcast real-time parameters related to dams and weather conditions. Initially, rainfall sensors, waterfall sensors, flow sensors, and ultrasonic sensors are used to sense the water quantity, dam vacant level, and flow of water in the dam. Data collected by sensors placed at various points on the dam are sent to an Internet-connected Arduino. And, the spiking neural network (SNN) for predicting rainfall using previous year data from the meteorology dataset. Data collected from sensor nodes are used for monitoring and emission sensors are used to control gate opening to specified limits. All decisions are made from the central control room, which observes the situation in all areas and then issues orders to the various local control rooms. Finally, the proposed IDISense app is used to give alerts to neighboring people. The experimental result shows the proposed IDISense app compared with CNN, Resnet, Alexnet, and SNN. In one day, the SNN achieves an overall accuracy is 93.4% and in four days the SNN achieves an overall accuracy is 95.9%.