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Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series

Department of Agricultural and Forest Engineering, University of Lleida, 25198 Lleida, Spain
Department of Environment and Soil Science, E.T.S.E. Agraria, University of Lleida, 25198 Lleida, Spain
Geo-Information and Remote Sensing Group, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
Author to whom correspondence should be addressed.
Water 2019, 11(8), 1691;
Received: 26 June 2019 / Revised: 8 August 2019 / Accepted: 10 August 2019 / Published: 15 August 2019
(This article belongs to the Section Hydrology)
PDF [1027 KB, uploaded 15 August 2019]


The analyses of water resources availability and impacts are based on the study over time of meteorological and hydrological data trends. In order to perform those analyses properly, long records of continuous and reliable data are needed, but they are seldom available. Lack of records as in gaps or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management. Flow data series from gauging stations are not an exception. Over the last 20 years, forecasting models based on artificial neural networks (ANNs) have been increasingly applied in many fields of natural resources, including hydrology. This paper discusses results obtained on the application of cascade-correlation ANN models to predict daily water flow using Julian day and rainfall data provided by nearby weather stations in the Ebro river watershed (Northeast Spain). Five unaltered gauging stations showing a rainfall-dominated hydrological regime were selected for the study. Daily flow and weather data series covered 30 years to encompass the high variability of Mediterranean environments. Models were then applied to the in-filling of existing gaps under different conditions related to the characteristics of the gaps (6 scenarios). Results showed that when short periods before and after the gap are considered, this is a useful approach, although no general rule applied to all stations and gaps investigated. Models for low-water-flow periods provided better results (r = 0.76–0.8). View Full-Text
Keywords: artificial neural networks; water flow model; hydrological data; data gaps artificial neural networks; water flow model; hydrological data; data gaps

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Vega-Garcia, C.; Decuyper, M.; Alcázar, J. Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series. Water 2019, 11, 1691.

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