IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 35th IAHR Congress - Chengdu (2013) : THEME 7 - WATER RESOURCES AND HYDROINFORMATICS : Coupling Satellite Rainfall Estimates and Machine Learning Techniques for Flow Forecast: Application...
Coupling Satellite Rainfall Estimates and Machine Learning Techniques for Flow Forecast: Application to a Large Catchment in Southern Africa
Author : Jos‚ P. Matos, Th‚odora Cohen Liechti, Maria M. Portela and Anton J. Schleiss
Accurate river flow forecasting is an important asset for stream and reservoir management, being often translated into substantial social, economic and ecological gains. This contribution aims at coupling satellite rainfall estimates and machine learning techniques for daily flow forecast. Two lead times, of 30 and 60 days, were tested for flows at Victoria Falls, in Southern Africa. Six distinct machine learning models were compared with optimized ARMA models and benchmarked against a Fourierseries approximation. Results show that the addition of rainfall data generally enhanced the performances of machine learning models at 30 days but did not improve forecasts at 60 days. Also, it was shown that traditional ARMA models do not make use of the rainfall information. Regarding a lead time of 60 days, the machine learning models appear to bear great advantages compared to ARMA models which, for such a lead time have shown practically no forecast capabilities.
File Size : 1,296,503 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 35th IAHR Congress - Chengdu (2013)
Date Published : 19/07/2016
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