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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Veröffentlicht in:Stochastic environmental research and risk assessment Jg. 33; H. 2; S. 481 - 514
Hauptverfasser: Papacharalampous, Georgia, Tyralis, Hristos, Koutsoyiannis, Demetris
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019
Springer Nature B.V
Schlagworte:
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
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Zusammenfassung: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 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 extensive computational experiments based on simulations. Each of these experiments uses 2000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the forecasting performance of the methods using 18 metrics. The results indicate that stochastic and ML methods may produce equally useful forecasts.
Bibliographie:ObjectType-Article-1
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ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-018-1638-6