H087
Information Inference of Hydrological Events Using Artificial Intelligence

Session ID#: 74102

Session Description:
The conventional hydrological models suffer from certain shortcomings such as the tedious calibration time as well as the uncertainty in the estimation of hydrological variables. Moreover, the data deluge from meteorological observations calls for a nimble interpretation scheme to disentangle the intricate relationship of hydrological components involved in a hydrological event. Supported by the prominent achievements in computer science, artificial Intelligence has now emerged as a powerful data-driven tool to reveal the underlying inter-relations of the complex and highly nonlinear physical processes of the hydrological events, and hence, has served as an efficient and well-equipped alternative to approximate the conventional hydrological models for an accurate simulation of hydrological events. Being backed by the advanced theories in optimization theory, artificial intelligence – and in particular machine learning – has been able to tackle the aforementioned deficiencies of the hydrological models and propose promising learning-based predictive models.
Index Terms:

1816 Estimation and forecasting [HYDROLOGY]
1922 Forecasting [INFORMATICS]

Primary Convener:  Hamidreza Ghasemi Damavandi, Arizona State University, Future H2O, Knowledge Enterprise Development, Tempe, AZ, United States
Conveners:  Benjamin L Ruddell, Northern Arizona University, School of Informatics, Computing, and Cyber Systems, Flagstaff, United States, Hoshin Vijai Gupta, University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States and Grey Stephen Nearing, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Primary Liaison:  Hamidreza Ghasemi Damavandi, Arizona State University, Future H2O, Knowledge Enterprise Development, Tempe, AZ, United States
 
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See more of: Hydrology
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