Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes

Research within the field of hydrology often focuses on the statistical problem of comparing stochastic to machine learning (ML) forecasting methods. The performed comparisons are based on case studies, while a study providing large-scale results on the subject is missing. Herein, we compare 11 stoc...

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Bibliographic Details
Published in:	Stochastic environmental research and risk assessment Vol. 33; no. 2; pp. 481 - 514
Main Authors:	Papacharalampous, Georgia, Tyralis, Hristos, Koutsoyiannis, Demetris
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019 Springer Nature B.V
Subjects:	Aquatic Pollution Artificial intelligence Chemistry and Earth Sciences Computational Intelligence Computer applications Computer Science Computer simulation Earth and Environmental Science Earth Sciences Environment Forecasting Hydrology Learning algorithms Machine learning Math. Appl. in Environmental Science Original Paper Physics Probability Theory and Stochastic Processes Production methods River discharge River flow Rivers Statistics for Engineering Stochastic processes Time series Waste Water Technology Water Management Water Pollution Control Support vector machines No free lunch theorem Stochastic hydrology River discharge Random forests Time series
ISSN:	1436-3240, 1436-3259
Online Access:	Get full text
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