H32A:
Artificial Intelligence and Machine Learning Methods in Water Resources Management I


Session ID#: 10629

Session Description:
Climate variability and change is expected to affect the frequency of extreme hydrological events.  Floods and droughts are likely to happen more frequently with much higher intensity and extended period of time which pose challenges to water resources management and environmental sustainability.  Many studies have been shown that recent development in artificial intelligence and machine learning methods has enabled us to utilize large amount of in situ and remote sensing information more effectively for improving weather and climate prediction, hydrological forecasting, and ecological modeling. This session aims to bring together scientists to discuss and exchange knowledge about the current state of AI & machine learning approaches and to demonstrate their methods and applications in the hydrology, ecology, atmospheric and environmental sciences.
Primary Convener:  Kuo-lin Hsu, University of California Irvine, Irvine, CA, United States
Conveners:  Fi-John Chang, National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan and Li-Chiu Chang, Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan
Chairs:  Kuo-lin Hsu, University of California Irvine, Irvine, CA, United States, Fi-John Chang, National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan and Li-Chiu Chang, Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan
OSPA Liaison:  Li-Chiu Chang, Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan

Cross-Listed:
  • A - Atmospheric Sciences
  • NH - Natural Hazards
Index Terms:

1914 Data mining [INFORMATICS]
1918 Decision analysis [INFORMATICS]
1922 Forecasting [INFORMATICS]
1942 Machine learning [INFORMATICS]

Abstracts Submitted to this Session:

Data-Driven Techniques for Regional Groundwater Level Forecasts (71187)
Fi-John Chang1, Li-Chiu Chang2, Fong-He Tsai1 and Hung-Yu Shen3, (1)National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan, (2)Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan, (3)Tamkang University, Taipei, Taiwan
Developing an Intelligent Reservoir Flood Control Decision Support System through Integrating Artificial Neural Networks (84333)
Li-Chiu Chang1, I-Feng Kao1, Fong-He Tsai2, Hung-Cheng Hsu3, Shun-Nien Yang1, Hung-Yu Shen1 and Fi-John Chang2, (1)Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan, (2)National Taiwan University, Department of Bioenvironmental Systems Engineering, Taipei, Taiwan, (3)Taiwan Shihmen Irrigation Association, Taoyuan City, Taiwan
Drought monitoring using downscaled soil moisture through machine learning approaches over North and South Korea (72112)
Seonyoung Park1, Jungho Im1, Jinyoung Rhee2 and Sumin Park1, (1)Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South), (2)APEC Climate Center, Busan, Korea, Republic of (South)
Development of Global Precipitation Estimation System Using Artificial Neural Network Models (79649)
Kuo-lin Hsu, University of California Irvine, Irvine, CA, United States
Non-linear Regression and Machine Learning for Streamflow Prediction and Climate Change Impact Analysis (82149)
Seth Guikema, University of Michigan Ann Arbor, Ann Arbor, MI, United States, Julie Elizabeth Shortridge, Virginia Tech, Blacksburg, VA, United States and Benjamin F Zaitchik, Johns Hopkins University, Baltimore, MD, United States
Forecasting Precipitation over the MENA Region: A Data Mining and Remote Sensing Based Approach (82798)
Racha Elkadiri1,2, Mohamed Sultan3, Tamer Elbayoumi4 and Kyle Chouinard2, (1)Middle Tennessee State University, Murfreesboro, TN, United States, (2)Western Michigan University, Kalamazoo, MI, United States, (3)Western Michigan University, Department of Geological and Environmental Sciences, Kalamazoo, MI, United States, (4)Oklahoma State University, Stillwater, OK, United States
Simulating California Reservoir Operation Using the Classification and Regression Tree Algorithm Combined with a Shuffled Cross-Validation Scheme (65416)
Tiantian Yang1, Xiaogang Gao1, Soroosh Sorooshian2 and Xin Li3, (1)University of California Irvine, Irvine, CA, United States, (2)University of California, Irvine, Department of Civil and Environmental Engineering, Irvine, United States, (3)Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Beijing, China

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