Facing Losses of Telemetric Signal in Real Time Forecasting of Water Level using Artificial Neural Networks

Juliano Santos Finck () and Olavo Correa Pedrollo ()
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Juliano Santos Finck: Instituto de Pesquisas Hidráulicas, Universidade Federal Do Rio Grande Do Sul
Olavo Correa Pedrollo: Instituto de Pesquisas Hidráulicas, Universidade Federal Do Rio Grande Do Sul

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 3, No 21, 1119-1133

Abstract: Abstract Real-time forecasting plays a valuable role in the early warning system framework by reducing damage. However, signal loss in telemetric monitoring networks tends to occur during extreme events, precisely when data are needed for forecasting. We present an original approach, consisting of a tree of artificial neural networks (ANNs), with complete and partial models to deal with signal loss scenarios, where we also tested a new type of filter (GWMA – Gamma-Weighted Moving Average) to aggregate data in time and reduce the number of model inputs. In addition to this filter, we tested UWMA (Uniformly Weighted Moving Average), EWMA (Exponentially Weighted Moving Average) and MD (Moving Difference). Novel concepts were used to reduce ANN internal complexity and to identify a training dataset size corresponding to an ideal amount of information, which does not hinder training. We developed a model to forecast the water level up to 24 h ahead at Encantado, in the Taquari-Antas River basin, southern Brazil. The data period comprises hourly records from 26/11/2015 to 24/04/2019. The verification dataset performances of the partial models are compared to the complete model, indicating no substantial loss. The mean absolute error and the Nash–Sutcliffe of the complete model for the lead times of 4, 10, and 20 h are 5.4, 17.7, and 19.4 cm; and 0.99, 0.95, and 0.92, respectively. Therefore, the ANN tree is confirmed as a viable alternative to cope with signal loss scenarios.

Keywords: Artificial intelligence; Machine learning; Taquari-antas river basin; Telemetry system; Hydrological model (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11269-021-02782-x

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