H030
Artificial Intelligence and Machine Learning for Water Quality Monitoring Using Remote Sensing

Session ID#: 162398

Session Description:
Water quality monitoring is crucial for water resources protection and management. Compared with traditional costly sampling-based monitoring, remote sensing (RS) supports full-coverage, periodic, quick-access, and cost-effective water quality monitoring. Over the past several decades, the continuing development of satellite sensors provides imagery of higher and higher spatial, spectral, and temporal resolution, which lays the foundation for water quality monitoring. At present, remotely monitoring technology of optically active parameters (e.g., Chlorophyll-a, etc.) tends to be mature. Scholars begin to focus on non-optically active parameters (e.g., total phosphorus, etc.). With the rapid development of artificial intelligence, machine learning is expected to further improve the monitoring performance of water quality parameters (especially non-optically active parameters). This session aims to explore the most recent progress made in terms of artificial intelligence applied in water quality monitoring using RS and will provide interactive discussions on the application-based results.
Index Terms:

1855 Remote sensing [HYDROLOGY]
1942 Machine learning [INFORMATICS]

Primary Convener:  Jinhui Jeanne Huang, Nankai University, Tianjian, China
Conveners:  Hongwei Guo, Nankai University, Environmental Science and Engineering, Tianjin, China and Xiaotong Zhu, Nankai University, Tianjian, China
 
Use of Multiple Sensor Satellite Images to Predict the Concentration of Chlorophyll-a within Wetlands using Artificial Neural Networks (1061091)
Byeongwon Lee1, Sangchul Lee2, Hyemin Jeong1, Dongho KIM1 and Yongsung Kwon1, (1)University of Seoul, Seoul, Korea, Republic of (South), (2)USDA-ARS, Beltsville, United States
 
Enhancement of Spatial resolution with Deep Learning-based Super-Resolution models for Inland Harmful Algal Bloom (HABs) monitoring (1065303)
DoHyuck Kwon1, Seokmin Hong1, Ather Abbas2, Sanghyun Park3, Gibeom Nam3, Jae-Hyun Yoo3, Kyunghyun Kim3, Hong tae Kim3, JongCheol Pyo4 and Kyung Hwa Cho5, (1)Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South), (2)Ulsan National Institute of Science and Technology, Ulsan, South Korea, (3)National Institute of Environmental Research, Incheon, South Korea, (4)Korea Environment Institute, Sejong, South Korea, (5)School of Urban and Environmental Engineering, Ulsan National Institute Science and Technology (UNIST), Ulsan, South Korea
 
Artificial Intelligence for Water Resources Conservation: Evidence Landscape of Empirical Research (1122165)
Mohammad Imroz, TERI School of Advanced Studies, New Delhi, India and Md Azharuddin, Jamia Hamdard, Pharmaceutical Medicine, New Delhi, India
 
Assessing the Spatiotemporal Change of Surface Water quality in Lake Arlington: Integrated Satellite-UAV Remote Sensing and In-situ Data. (1181036)
Benite Ishimwe and Esayas Gebremichael, Texas Christian University, Geological Sciences, Fort Worth, TX, United States
 
Analysis of Water Surface Reflectance Characteristics Based on Hyperspectral Images for Chlorophyll-a Estimation of Baekje Weir and Namyang Lake in South Korea (1191130)
Wonjin Jang1, Jeehun Chung1, Jinuk KIM1, Yongeun Park2 and Seongjoon Kim2, (1)Konkuk University, Graduate School, Department of Civil, Environmental and Plant Engineering, Seoul, South Korea, (2)Konkuk University, College of Engineering, Division of Civil and Environmental Engineering, Seoul, South Korea
 
Applying Open Innovation and Multidisciplinary Machine Learning Insights to Address Harmful Algal Blooms (1194651)
Emily Gelbart, Polytechnic School of New York University, Brooklyn, NY, United States and Shobhana Gupta, Agile Decision Sciences, Huntsville, United States
See more of: Hydrology
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