Applying cascade-correlation neural networks to in-fill gaps in Mediterranean daily flow data series

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Issue date
2019-08-15Suggested citation
Vega García, Cristina;
Decuyper, Mathieu;
Alcázar Montero, Jorge;
.
(2019)
.
Applying cascade-correlation neural networks to in-fill gaps in Mediterranean daily flow data series.
Water, 2019, vol. 11, num. 8, article number 1691.
https://doi.org/10.3390/w11081691.
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Show full item recordAbstract
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).