A UAV-Assisted FP-xLSTM Framework for Spatio-Temporal Prediction of Multi-Depth Urban River Water Quality

Effective water quality monitoring is essential for protecting aquatic environments and supporting sustainable management of urban rivers. Accurate prediction and visualization of water quality parameters (WQPs) distributions across spatial, temporal, and vertical dimensions remained challenging for unmanned aerial vehicle (UAV)-based remote sensing approaches, which often neglected depth-wise variability and required extensive water sampling to maintain stable prediction performance for multiple WQPs. To address these limitations, this study proposed a feature propagation-based extended long short-term memory framework (FP-xLSTM) for unified prediction and visualization of multi-depth WQPs, including total phosphorus (TP), total nitrogen (TN), chlorophyll-a (Chl-a), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and total suspended solids (TSS). By integrating spectral characteristics with spatio-temporal dependencies through feature propagation across consecutive temporal observations, FP-xLSTM effectively captured short-term fluctuations and learned vertical distribution patterns of heterogeneous WQPs from multi-source data while reducing sampling requirements. A field study with an eight-month interval between the first and last sampling campaigns was conducted to evaluate the proposed framework under cross-region experimental settings and against state-of-the-art baseline models. Results demonstrated that FP-xLSTM consistently outperformed competing methods, achieving average coefficients of determination (R2) ranging from 0.66 to 0.95 and mean absolute percentage errors (MAPE) between 9.33% and 19.06%. Furthermore, the framework has potential for deployment in practical environmental monitoring applications to support local environmental authorities in tracking river water quality dynamics and optimizing management strategies. Overall, the proposed method provided an effective and scalable intelligent framework for UAV-assisted urban water quality assessment and multi-depth environmental visualization.