Artificial Intelligence-Assisted IoT Model for Water Level Monitoring and Prediction Systems: A Review and Analysis

Abstract

Flood disasters have become increasingly frequent and severe due to climate change and urban expansion. Traditional water level monitoring systems often lack real-time data processing and predictive capabilities. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) presents a promising solution for enhancing water level monitoring and flood prediction systems. This paper provides a comprehensive review and analysis of AI-assisted IoT models for water level monitoring and prediction. It examines system architectures, sensor networks, and the application of AI algorithms such as Fuzzy Logic and Long Short-Term Memory (LSTM) networks. The study highlights the benefits of combining real-time IoT data with AI-based predictive models to improve the accuracy and responsiveness of flood early warning systems. Challenges related to data quality, sensor network infrastructure, and model optimization are also discussed. This review aims to inform future research and development in intelligent disaster mitigation systems.