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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Acknowledgements
This research is funded by Xi’an Jiaotong-Liverpool University Urban and Environmental Studies University Research Center, Grant number RDH-101-2022-0032.
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Fu, L., Lo, Y., Lu, T.C., Zhang, C. (2024). Water Quality Inversion of UAV Multispectral Data Using Machine Learning. In: Papadikis, K., Zhang, C., Tang, S., Liu, E., Di Sarno, L. (eds) Towards a Carbon Neutral Future. ICSBS 2023. Lecture Notes in Civil Engineering, vol 393. Springer, Singapore. https://doi.org/10.1007/978-981-99-7965-3_31
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DOI: https://doi.org/10.1007/978-981-99-7965-3_31
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