Hydrological measurement, Flood prediction, BeiDou, Deep learning, CNN-LSTM, Kalman filtering, Computational linguistics. Natural language processing, P98-98.5, Electronic computers. Computer science, QA75.5-76.95

Abstract Accurate hydrological measurement is essential for flood prediction. As per the early investigations, most modern methods struggle with significant limitations regarding precise monitoring and tracking ability. To address these challenges, we suggest using the proposed HydroVisionNet-A HiFi AI-driven intelligent hydrological measurement approach that integrates the benefits of deep learning and BeiDou high precision positioning. HiFi net involves CNN for spatial feature extraction to detect the exact water level variations and hydrological changes from real-time image samples. Where the RNN model analyses the time-series hydrological data to predict flood risks. The Kalman filtering is the additional advantage that helps refine the sensor inputs to reduce the noise for consistent predictions. The suggested model is validated through the real-time BeiDou hydrometeorological data from the Tibetan plateau, which involves well adequate hydrology data, high-resolution rainfall, soil moisture, various lake types and meteorological observations. The results show the proposed HiFi-Net’s real-time efficacy in three particular regions of AWS-YCW, AWS-SDZ, and AWS-DHY with the RMSE scores of 0.400 m, 0.466 m, and 0.512 m, respectively. This study is an advanced fusion model that integrates effective novel techniques by integrating the existing abilities of the model. It also serves as a powerful solution to address future hydrological risks efficiently.