UAV Hyperspectral Retrieval of Optically Inactive Water Quality Parameters (Total Hardness and CODMn) Using a GA-Optimized Attention-Enhanced Neural Network

Retrieving non-optically active water quality variables, such as total hardness (TH) and permanganate index (CODMn), from hyperspectral data remains challenging because these parameters are not directly linked to spectral reflectance. To improve their estimation from UAV hyperspectral imagery, a GA-MHSA-BPNN framework was developed by combining a genetic algorithm (GA), multi-head self-attention (MHSA), and a backpropagation neural network (BPNN). In this framework, MHSA was introduced to strengthen the representation of informative spectral features, while GA was applied to optimize the initial network parameters and thus enhance convergence stability. The proposed framework was evaluated against BPNN, GA-BPNN, MHSA-BPNN, and 1D-CNN models. Among the tested approaches, GA-MHSA-BPNN produced the most favorable results for both TH and CODMn, with R2 values of 0.878 and 0.843, respectively. Additional experiments using different proportions of training samples showed that the model remained relatively stable when the training data were reduced to 70% and 50% of the original dataset. These results indicate that integrating GA and MHSA into a UAV hyperspectral retrieval framework can improve the estimation of non-optically active water quality variables and provide useful methodological support for efficient and refined monitoring of drinking water source areas.