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

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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)

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.

Suggested Citation

  • Juliano Santos Finck & Olavo Correa Pedrollo, 2021. "Facing Losses of Telemetric Signal in Real Time Forecasting of Water Level using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1119-1133, February.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:3:d:10.1007_s11269-021-02782-x
    DOI: 10.1007/s11269-021-02782-x
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    References listed on IDEAS

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    1. Zhangjun Liu & Shenglian Guo & Honggang Zhang & Dedi Liu & Guang Yang, 2016. "Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2111-2126, May.
    2. Kai Lun Chong & Sai Hin Lai & Yu Yao & Ali Najah Ahmed & Wan Zurina Wan Jaafar & Ahmed El-Shafie, 2020. "Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2371-2387, June.
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    4. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2631-2689, September.
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    1. Xuan Wang & Wenchong Tian & Zhenliang Liao, 2022. "Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4201-4217, September.

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