Machine Learning-Based Ensemble Prediction of Water-quality Variables Using Feature-level and Decision-level Fusion with Proximal Remote Sensing

The objectives of this study were to accurately model relationships between spectral reflectance and water-quality parameters, including blue-green algae phycocyanin, chlorophyll a, total suspended solids, turbidity, and total dissolved solids; evaluate feature-level fusion to spectral data for water-quality modeling; and evaluate the effectiveness of machine learning regression techniques and decision-level fusion for water-quality variable prediction. We introduce the application of canonical correlation analysis fusion as a method for water-based spectral analysis to overcome the low signal-to-noise ratio of the data. Water-quality variables and spectral reflectance were used to create predictive models via machine learning regression models, including multiple linear regression, partial least-squares regression, Gaussian process regression, support vector machine regression, and extreme learning machine regression. The models were then combined using decision-level fusion. Results indicate that canonical correlation analysis feature-level fusion and machine learning techniques are superior to traditional methods.