IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i3d10.1007_s11269-021-02782-x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02782-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-021-02782-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    3. Cools, Jan & Innocenti, Demetrio & O’Brien, Sarah, 2016. "Lessons from flood early warning systems," Environmental Science & Policy, Elsevier, vol. 58(C), pages 117-122.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ciurea Iulia-Cristina, 2024. "The Impact of the EU AI Act on the UN Sustainable Development Goals for 2030 – A Text Analysis," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 2857-2870.
    2. Isabel Kaufmann Almeida & Aleska Kaufmann Almeida & Jorge Luiz Steffen & Teodorico Alves Sobrinho, 2016. "Model for Estimating the Time of Concentration in Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4083-4096, September.
    3. Jiazheng Lu & Jun Guo & Li Yang & Xunjian Xu, 2017. "Research of reservoir watershed fine zoning and flood forecasting method," 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. 89(3), pages 1291-1306, December.
    4. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    5. Vieri Tarchiani & Giovanni Massazza & Maurizio Rosso & Maurizio Tiepolo & Alessandro Pezzoli & Mohamed Housseini Ibrahim & Gaptia Lawan Katiellou & Paolo Tamagnone & Tiziana De Filippis & Leandro Rocc, 2020. "Community and Impact Based Early Warning System for Flood Risk Preparedness: The Experience of the Sirba River in Niger," Sustainability, MDPI, vol. 12(5), pages 1-24, February.
    6. Koirala, Pankaj & Kotani, Koji & Managi, Shunsuke, 2022. "How do farm size and perceptions matter for farmers’ adaptation responses to climate change in a developing country? Evidence from Nepal," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 188-204.
    7. Deolfa Josè Moisès & Nnenesi Kgabi & Olivia Kunguma, 2023. "Integrating “Top-Down” and “Community-Centric” Approaches for Community-Based Flood Early Warning Systems in Namibia," Challenges, MDPI, vol. 14(4), pages 1-17, October.
    8. Katerina Trepekli & Thomas Balstrøm & Thomas Friborg & Bjarne Fog & Albert N. Allotey & Richard Y. Kofie & Lasse Møller-Jensen, 2022. "UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment," 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. 113(1), pages 423-451, August.
    9. Sagar Ratna Bajracharya & Narendra Raj Khanal & Pashupati Nepal & Sundar Kumar Rai & Pawan Kumar Ghimire & Neera Shrestha Pradhan, 2021. "Community Assessment of Flood Risks and Early Warning System in Ratu Watershed, Koshi Basin, Nepal," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    10. Zerina Lokmic-Tomkins & Dinesh Bhandari & Chris Bain & Ann Borda & Timothy Charles Kariotis & David Reser, 2023. "Lessons Learned from Natural Disasters around Digital Health Technologies and Delivering Quality Healthcare," IJERPH, MDPI, vol. 20(5), pages 1-28, March.
    11. Elkady, Sahar & Hernantes, Josune & Labaka, Leire, 2023. "Towards a resilient community: A decision support framework for prioritizing stakeholders' interaction areas," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. Shu, Xingsheng & Ding, Wei & Peng, Yong & Wang, Ziru, 2024. "Value of long-term inflow forecast for hydropower operation: A case study in a low forecast precision region," Energy, Elsevier, vol. 298(C).
    13. Shivam Gupta & Sachin Modgil & Ajay Kumar & Uthayasankar Sivarajah & Zahir Irani, 2022. "Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations," Post-Print hal-04325638, HAL.
    14. Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.
    15. Dianyou Yu & Zheng He, 2022. "Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities," 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. 112(1), pages 1-36, May.
    16. Dehai Zhu & Qian Cao, 2023. "Two determination models of slope failure pattern based on the rainfall intensity–duration early warning threshold," 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. 118(3), pages 1917-1931, September.
    17. Brielle Lillywhite & Gregor Wolbring, 2022. "Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review," Sustainability, MDPI, vol. 14(20), pages 1-50, October.
    18. Xingsheng Shu & Wei Ding & Yong Peng & Ziru Wang & Jian Wu & Min Li, 2021. "Monthly Streamflow Forecasting Using Convolutional Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5089-5104, December.
    19. Y. Supriya & Thippa Reddy Gadekallu, 2023. "Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    20. Sterre Bierens & Kees Boersma & Marc J. C. van den Homberg, 2020. "The Legitimacy, Accountability, and Ownership of an Impact-Based Forecasting Model in Disaster Governance," Politics and Governance, Cogitatio Press, vol. 8(4), pages 445-455.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:35:y:2021:i:3:d:10.1007_s11269-021-02782-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.