Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156772
Title: Benchmarking of hierarchical bayesian model aggregation, xgboost and optimal band ratio analysis (OBRA) models for total suspended sediments retrieval from remote sensing
Authors: Pak, Hui Ying
Law, Adrian Wing-Keung
Lin, Weisi
Keywords: Engineering::Civil engineering::Water resources
Engineering::Environmental engineering::Environmental protection
Issue Date: 2022
Source: Pak, H. Y., Law, A. W. & Lin, W. (2022). Benchmarking of hierarchical bayesian model aggregation, xgboost and optimal band ratio analysis (OBRA) models for total suspended sediments retrieval from remote sensing. 39th IAHR World Congress (IAHR 2022), 5395-5403. https://dx.doi.org/10.3850/IAHR-39WC2521716X2022procd
Conference: 39th IAHR World Congress (IAHR 2022)
Abstract: In remote sensing of water quality, the use of data-driven models for the retrieval of water quality parameters has been gaining traction in recent years, especially with the recent advancement in machine learning (ML). The application of deep learning ML models on satellite imageries for retrieving water quality parameters for large areas is also becoming ubiquitous, although in many cases, simpler ML models like band-ratio algorithms can also achieve similar performances because the limited bands available on most satellite imageries restrict high dimensional data for the ML models to exploit. With Unmanned Aerial Vehicles (UAVs) and hyperspectral sensors, however, fine spectral and spatial data can be obtained and there is a need for more advanced ML to be developed to capitalize on the richer information. In this study, a new method called Hierarchical Bayesian Model Aggregation for Optimal Multiple Band Ratio Analysis (HBMA-OMBRA) has been developed as a proof-of-concept for the optimal determination of Total Suspended Solids (TSS) concentrations from the hyperspectral data. A laboratory investigation was also conducted in the present study to measure the hyperspectral reflectance in experiments under various environmental conditions to verify the robustness of HBMA-OMBRA. The performance of the HBMA-OMBRA was benchmarked against the popular XGBoost, as well as a band-ratio algorithm – Optimal Band Ratio Analysis (OBRA). The overall results showed that HBMA-OMBRA performed the best among these models.
URI: https://hdl.handle.net/10356/156772
URL: https://cmswebonline.com/iahr2022/epro/html/stheme-06-09.html
ISBN: 978-90-832612-1-8
ISSN: 2521-716X
DOI: 10.3850/IAHR-39WC2521716X2022procd
Schools: Interdisciplinary Graduate School (IGS) 
School of Civil and Environmental Engineering 
School of Computer Science and Engineering 
Research Centres: Nanyang Environment and Water Research Institute 
Rights: © 2022 International Association for Hydro-Environment Engineering and Research (IAHR). All rights reserved. This paper was published in the Proceedings of 39th IAHR World Congress (IAHR 2022) and is made available with permission of International Association for Hydro-Environment Engineering and Research (IAHR).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Conference Papers
IGS Conference Papers
NEWRI Conference Papers
SCSE Conference Papers

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