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2024 | OriginalPaper | Chapter

Water Quality Inversion of UAV Multispectral Data Using Machine Learning

Authors : L. Fu, Y. Lo, T. C. Lu, C. Zhang

Published in: Towards a Carbon Neutral Future

Publisher: Springer Nature Singapore

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Abstract

The majority of water quality inversions rely on satellite data with poor spectral resolution. Satellite data is tougher to obtain for a specific date and less timely than UAV data due to transit cycles and weather. This method of inferring water quality from UAV multispectral data is based on the use of machine learning. With high resolution, low flying altitude, low cost, and good performance, UAV multispectral data synchronizes with sampling point water body parameters. Studies on inverting water quality is difficult due to the need for a specific inversion model for each location and set of circumstances. In order to improve water quality inversion results and get around the limitations of linear models, machine learning is being used more and more. For efficient and quick water quality monitoring in Yuandang Lake, use machine learning to invert various water quality indicators, compare the results, and select the appropriate indicators.

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Literature
Cui M, Sun Y, Huang C, Li M (2022) Water turbidity retrieval based on UAV hyperspectral remote sensing. Water 14(1):128CrossRef
Gao M, Li J, Wang S, Zhang F, Yan K, Yin Z, ..., Shen W (2022) Smartphone–camera–based water reflectance measurement and typical water quality parameter inversion. Remote Sens 14(6):1371
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90CrossRef
Lei F, Yu Y, Zhang D, Feng L, Guo J, Zhang Y, Fang F (2020) Water remote sensing eutrophication inversion algorithm based on multilayer convolutional neural network. J Intell Fuzzy Syst 39(4):5319–5327CrossRef
Nyambar INA, Mohan Viswanathan P (2023) Assessment on urban lakes along the coastal region of Miri, NW Borneo: implication for hydrochemistry, water quality, and pollution risk. Environ Sci Pollut Res 1–23
Wang Z, Zhang Y, Chen Z, Yang H, Sun Y, Kang J, ..., Liang X (2016) Application of ReliefF algorithm to selecting feature sets for classification of high resolution remote sensing image. In: 2016 IEEE international geoscience and remote sensing symposium (IGARSS). IEEE, pp 755–758
Wang L, Yue X, Wang H, Ling K, Liu Y, Wang J, ..., Song H (2020) Dynamic inversion of inland aquaculture water quality based on UAVs-WSN spectral analysis. Remote Sens 12(3):402
Xu Y, Xu T (2022) An evolving marine environment and its driving forces of algal blooms in the Southern Yellow Sea of China. Mar Environ Res 178:105635CrossRef
Yang H, Kong J, Hu H, Du Y, Gao M, Chen F (2022) A review of remote sensing for Water Quality Retrieval: Progress and challenges. Remote Sens 14(8):1770CrossRef
Zhang H, Zhang L, Wang S, Zhang L (2022) Online water quality monitoring based on UV–Vis spectrometry and artificial neural networks in a river confluence near Sherfield-on-Loddon. Environ Monit Assess 194(9):630CrossRef
Zhou J (2021) Dodge or disinfect? Classifying algae in small-scale water bodies via low-cost deep neural networks. In: 2021 IEEE 3rd international conference on frontiers technology of information and computer (ICFTIC). IEEE, pp 263–277
Zhu H, Jiang Y, Zhang C, Liu S (2022) Research on mosaic method of UAV low-altitude remote sensing image based on sift and surf. In: J Phys Conf Ser, 2203(1):012027. IOP Publishing
Metadata
Title
Water Quality Inversion of UAV Multispectral Data Using Machine Learning
Authors
L. Fu
Y. Lo
T. C. Lu
C. Zhang
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Towards a Carbon Neutral Future

Print ISBN: 978-981-9979-64-6

Electronic ISBN: 978-981-9979-65-3

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-99-7965-3_31