TY - GEN
T1 - Water Quality Inversion of UAV Multispectral Data Using Machine Learning
AU - Fu, L.
AU - Lo, Ying
AU - Lu, T. C.
AU - Zhang, C.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Machine learning
KW - UAV multispectral data
KW - Water quality inversion
UR - http://www.scopus.com/inward/record.url?scp=85189553326&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7965-3_31
DO - 10.1007/978-981-99-7965-3_31
M3 - Conference Proceeding
AN - SCOPUS:85189553326
SN - 9789819979646
T3 - Lecture Notes in Civil Engineering
SP - 357
EP - 365
BT - Towards a Carbon Neutral Future - The Proceedings of The 3rd International Conference on Sustainable Buildings and Structures
A2 - Papadikis, Konstantinos
A2 - Zhang, Cheng
A2 - Tang, Shu
A2 - Liu, Engui
A2 - Di Sarno, Luigi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Sustainable Buildings and Structures, ICSBS 2023
Y2 - 17 August 2023 through 20 August 2023
ER -