Abstract: A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting (2016 AGU Fall Meeting)

A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

Taereem Kim^1,2, Kyungwon Joo³, Jungho Seo⁴ and Jun-Haeng Heo¹, (1)Yonsei University, Seoul, Korea, Republic of (South), (2)University of Oklahoma Norman Campus, School of Civil Engineering and Environmental Science, Norman, United States, (3)Yonsei University, Seodaemun, Korea, Republic of (South), (4)Yonsei University, Department of Civil and Environmental Engineering, Seoul, South Korea

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