Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data

Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data

Urban rivers play an essential role in the human environment and urban development; because of their narrow and long characteristics, challenging for general remote sensing data sources to meet the monitoring requirements. In order to solve the problem of insufficient application of remote sensing w...

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Bibliographic Details
Main Authors: Chen, B. (Author), Chen, P. (Author), Choi, J. (Author), Mu, X. (Author), Park, H. (Author), Wang, B. (Author), Wu, Y. (Author), Xu, S. (Author), Yang, H. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
Adaptive boosting
Ammonia
Antennas
Boosting algorithm
chlorophyll a
Decision trees
Deep neural networks
environmental monitoring
environmental protection
Environmental technology
Genetic algorithms
Gradient boosting
Human environment
machine learning
Machine learning
Multi-spectral data
Nitrogen
polymorphism
remote sensing
Remote sensing
Remote-sensing
River pollution
Rivers
Semantics
turbidity
UAV remote sensing
Unmanned aerial vehicle remote sensing
Unmanned aerial vehicles (UAV)
Urban development
Urban growth
Urban river
water quality
Water quality
Water quality inversion
Water quality parameters
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