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

Wednesday, 16 December 2015: 10:20-12:20
3016 (Moscone West)
Primary Conveners:  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 Liaisons:  Li-Chiu Chang, Tamkang University, Department of Water Resources and Environmental Engineering, Taipei, Taiwan
10:20
H32A-01
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
10:35
H32A-02
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 Yang4 and Hung-Yu Shen1, (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, (4)Tamkang University, Taipei, Taiwan
10:50
H32A-03
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, South Korea, (2)APEC Climate Center, Busan, South Korea
11:05
H32A-04
Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods (75695)
John Quilty and Jan F Adamowski, McGill University, Montreal, QC, Canada
11:20
H32A-05
Development of Global Precipitation Estimation System Using Artificial Neural Network Models (79649)
Kuo-lin Hsu, University of California Irvine, Irvine, CA, United States
11:35
H32A-06
Non-linear Regression and Machine Learning for Streamflow Prediction and Climate Change Impact Analysis (82149)
Seth Guikema1, Julie Shortridge2 and Benjamin F Zaitchik2, (1)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (2)Johns Hopkins University, Baltimore, MD, United States
11:50
H32A-07
Forecasting Precipitation over the MENA Region: A Data Mining and Remote Sensing Based Approach (82798)
Racha Elkadiri1, Mohamed Sultan1, Tamer Elbayoumi2 and Kyle Chouinard1, (1)Western Michigan University, Kalamazoo, MI, United States, (2)Oklahoma State University, Stillwater, OK, United States
12:05
H32A-08
Simulating California Reservoir Operation Using the Classification and Regression Tree Algorithm Combined with a Shuffled Cross-Validation Scheme (65416)
Tiantian Yang1, Xiaogang Gao2, Soroosh Sorooshian1 and Xin Li3, (1)University of California Irvine, Irvine, CA, United States, (2)Univ California, Irvine, Irvine, CA, United States, (3)CAREERI/CAS Cold and Arid Regions Environmental and Engineering Research Institute, Lanzhou, China
 
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