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.
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Document Type: Research Article
Publication date: April 1, 2019
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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