Multi-Algorithm Comparison for Water Quality Retrieval: Integrating Landsat-8 OLI and Machine Learning in Karst Plateau Reservoirs

Abstract

Chlorophyll a (Chla), total phosphorus (TP), total nitrogen (TN), and turbidity (Turb) are key indicators for assessing water eutrophication. To overcome the limitations of conventional regression methods, this study developed and compared inversion models for these parameters using Landsat-8 OLI imagery and field data, comparing multiple linear regression and seven machine learning algorithms: Genetic Algorithm- and Particle Swarm-optimized Backpropagation Neural Networks (BPNNs), Convolutional Neural Network (CNN), Extreme Learning Machine (ELM), Random Forest (RF), XGBoost, and Support Vector Regression (SVR). The results revealed that traditional regression performed better for optically active parameters (Chla and Turb) than for non-optically active ones (TP and TN), whereas machine learning models significantly improved accuracy, particularly for TP and TN. The XGBoost model achieved the highest performance (R2 > 0.90 for all parameters). Post-calibration analysis further delineated the spatial distributions and inter-parameter correlations in Pingzhai Reservoir, providing a robust method for water quality monitoring and assessment.