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Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms
- Source :
- Remote Sensing, Vol 13, Iss 3928, p 3928 (2021), Remote Sensing; Volume 13; Issue 19; Pages: 3928
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.
- Subjects :
- Elastic net regularization
Suspended solids
Computer science
business.industry
Science
water quality parameters inversion
machine learning
UAV-borne hyperspectral data
water quality mapping
Hyperspectral imaging
Inversion (meteorology)
Perceptron
Machine learning
computer.software_genre
General Earth and Planetary Sciences
Water quality
Artificial intelligence
Freshwater resources
business
Algorithm
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 13
- Issue :
- 3928
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....98e4599d7675937fa9101f99a56b3f69