首页|Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing

Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing

扫码查看
原文链接
  • NETL
  • NSTL
  • Iwa Publishing
Machine learning models for water quality prediction often face challenges due to insufficient data and uneven spatial-temporal distributions. To address these issues, we introduce a framework combining machine learning, numerical modeling, and remote sensing imagery to predict coastal water turbidity, a key water quality proxy. This approach was tested in the Great Lakes region, specifically Cleveland Harbor, Lake Erie. We trained models using observed data and synthetic data from 3D numerical models and tested them against in situ and remote sensing data from PlanetLabs' Dove satellites. High-resolution (HR) data improved prediction accuracy, with RMSE values of 0.154 and 0.146 logiO(FNU) and R2 values of 0.92 and 0.93 for validation and test datasets, respectively. Our study highlights the importance of unified turbidity measures for data comparability. The machine learning model demonstrated skill in predicting turbidity through transfer learning, indicating applicability in diverse, data-scarce regions. This approach can enhance decision support systems for coastal environments by providing accurate, timely predictions of water quality variables. Our methodology offers robust strategies for turbidity and water quality monitoring and has potential for improving input data quality for numerical models and developing predictive models from remote sensing data.

hydrodynamic modelingmachine learningremote sensingsolute transporttransfer learningwater turbidity

Saeed Memari、Mantha S. Phanikumar、Vishnu Boddeti、Narendra N. Das

展开 >

Department of Civil & Environmental Engineering, Michigan State University, East Lansing, Ml 48824, USA

Department of Civil & Environmental Engineering, Michigan State University, East Lansing, Ml 48824, USA||MSU AgBioResearch, East Lansing, Ml 48824, USA

Department of Computer Science & Engineering, Michigan State University, East Lansing, Ml 48824, USA

Department of Civil & Environmental Engineering, Michigan State University, East Lansing, Ml 48824, USA||Department of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, Ml 48824, USA

展开 >

2024

10.2166/hydro.2024.110

Journal of hydroinformatics

Journal of hydroinformatics

SCI
ISSN：1464-7141
年,卷(期)：2024.26(10/12)
  • 76