AI-driven Flood Surveillance and Dam Control: Advancing Resilience Through Data Science - Ushaa Eswaran; C. Pushpalatha; Shaik Beebi (International Journal of Data Structure Studies, 2023)
This study presents the development and real-world deployment of an intelligent system for flood monitoring and automated dam gate control using artificial intelligence (AI) and internet of things (IoT) sensors. Supervised machine learning models are developed to predict floods up to 48 h in advance. An automated dam gate operation system is designed to leverage the flood forecasts and real-time stream water levels for emergency control. The complete end-to-end infrastructure is installed across a flood-prone river basin spanning 50 km2, with sensors streaming data to an integrated software platform. The recurrent neural network model performs accurate hourly flood predictions across multiple lead times with an average precision of 0.82 and recall of 0.79 on the testing dataset. The automated dam gate system initiates opening/closing actions based on the flood predictions, while maintaining water levels within the safe operation zone 95.6% of the deployment duration. The end-to-end system provides emergency responders 36 h of early warning on average and reduces risk of infrastructure failures and downstream inundation.
Keywords: Flood monitoring; Internet of Things; Machine Learning; dam gate control; sensors