Water Quality Monitoring and Assessment of Inflow Rivers on a Central Island of Lake Taihu Using UAV Remote Sensing and Machine Learning

Lake Taihu is a vital source of surface water for the Yangtze River Delta region, so effective monitoring of its water quality is essential for protecting the water source. However, most existing studies on unmanned aerial vehicle (UAV)-based water quality remote sensing have focused on single large rivers or lakes, primarily employing validation methods involving randomly selected samples. This makes it difficult to assess the generalisability of the models to unfamiliar watercourses. This study focuses on 13 inflow rivers on Xishan Island, a central island in Lake Taihu, which are characterized by short flow paths, independent catchment areas, and varying land use influences. Using a UAV multispectral remote sensing platform, we have designed a water quality monitoring and assessment framework tailored to multi-river systems with small sample sizes. First, various water body indices were developed and analysed for correlation with measured water quality parameters. Then, machine learning algorithms such as Backpropagation (BP) neural networks, Random Forest, XGBoost, Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) were selected to construct retrieval models. For accuracy evaluation, a spatial independent validation strategy was employed whereby one sample was forcibly set aside from each river to constitute the validation set. Using this method, we generated spatial distribution maps of water quality parameters for the inflow rivers and evaluated the influencing factors of spatial variation in water quality by area, taking into account water body functional types and ecological characteristics. The experimental results indicate that under the conditions of spatial independent validation strategy, the SVM model achieved the highest retrieval accuracy for dissolved oxygen (R2 = 0.892, RMSE = 0.414 mg/L and MRE = 0.057), whereas the XGBoost model achieved the highest retrieval accuracy for turbidity (R2 = 0.853, RMSE = 0.632NTU and MRE = 0.065). The spatial pattern of water quality exhibited a pronounced gradient: dissolved oxygen (DO) concentrations followed the order of aquaculture area rivers > agricultural area rivers > urban area rivers, while turbidity displayed the opposite trend, reflecting that surrounding land use types, phytoplankton density, and human activity intensity are the dominant factors driving the spatial differentiation of river water quality on Xishan Island in spring. The full-chain technical framework of “multi-river synchronous retrieval—spatially independent validation strategy—area mechanistic assessment” proposed in this study provides a replicable evaluation paradigm for rapid water quality monitoring of Lake Taihu islands and similar watersheds, and holds significant implications for the construction of the Lake Taihu Eco-Island and the protection of the water environment.