[BMF18a] "Daily River Flow Prediction Based on Two Phase Constructive Fuzzy Systems Modeling: A Case of Hydrological - Meteorological Measurements Asymmetry

Revue Internationale avec comité de lecture : Journal of hydrology, pp. 255-265, 2018, (doi:j.jhydrol.2018.01.035)

Auteurs: B. Bou-Fakhreddine , I. Mougharbel , A. Faye , S. Abou-Chakra , Y. Pollet

motcle:

Résumé: Accurate daily river flow forecast is essential in many applications of water resources such as hydropower operation, agricultural planning and flood control. This paper presents a forecasting approach to deal with a newly addressed situation where hydrological data exist for a period longer than that of meteorological data (measurements asymmetry). In fact, one of the potential solutions to resolve measurements asymmetry issue is data re-sampling. It is a matter of either considering only the hydrological data or the balanced part of the hydro-meteorological data set during the forecasting process. However, the main disadvantage is that we may lose potentially relevant information from the left-out data. In this research, the key output is a Two-Phase Constructive Fuzzy inference hybrid model that is implemented over the non re-sampled data. The introduced modeling approach must be capable of exploiting the available data efficiently with higher prediction efficiency relative to Constructive Fuzzy model trained over re-sampled data set. The study was applied to Litani River in the Bekaa Valley â€“ Lebanon by using 4â€¯years of rainfall and 24â€¯years of river flow daily measurements. A Constructive Fuzzy System Model (C-FSM) and a Two-Phase Constructive Fuzzy System Model (TPC-FSM) are trained. Upon validating, the second model has shown a primarily competitive performance and accuracy with the ability to preserve a higher day-to-day variability for 1, 3 and 6â€¯days ahead. In fact, for the longest lead period, the C-FSM and TPC-FSM were able of explaining respectively 84.6% and 86.5% of the actual river flow variation. Overall, the results indicate that TPC-FSM model has provided a better tool to capture extreme flows in the process of streamflow prediction.

BibTeX

@article {
	BMF18a,
	title	=	"{"Daily River Flow Prediction Based on Two Phase Constructive Fuzzy Systems Modeling: A Case of Hydrological - Meteorological Measurements Asymmetry}",
	author	=	"B. Bou-Fakhreddine and I. Mougharbel and A. Faye and S. ABOU-CHAKRA and Y. Pollet",
	journal	=	"of hydrology",
	year	=	2018,
	pages	=	"255-265",
	doi	=	"j.jhydrol.2018.01.035",
}