Dispositivo configurable para modelar y analizar comportamientos de consumo de agua

  1. Álvarez Crespo, Marta María 1
  2. García-Fischer, Agustín 1
  3. Rubiños, Manuel 1
  4. Díaz-Longueira, Antonio 1
  5. Quintián, Héctor 1
  6. Calvo-Rolle, José Luis 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Journal:
Jornadas de Automática
  1. Cruz Martín, Ana María (coord.)
  2. Arévalo Espejo, V. (coord.)
  3. Fernández Lozano, Juan Jesús (coord.)

ISSN: 3045-4093

Year of publication: 2024

Issue: 45

Type: Article

DOI: 10.17979/JA-CEA.2024.45.10923 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

Abstract

This article outlines the fundamental guidelines needed to develop a didactic, modular, and scalable model to customize and collect data related to water consumption. This model will serve as a tool to achieve the goal of providing valuable information onwater consumption patterns and facilitate informed decision-making around the management of this vital resource. The concept is based on generating a set of individual modules capable of operating autonomously, which interconnect with each other, allowing for the expansion of the system, the generation of new configurations, and the addressing of new challenges. Additionally,the system supports the performance of different experiments and studies focused on the optimization of the processes under analysis. The results of these experiments and studies will contribute to a more efficient and sustainable management of water consumption.

Bibliographic References

  • Ahmed, S. S., Bali, R., Khan, H., Mohamed, H. I., Sharma, S. K., 10 2021. Improved water resource management framework for water sustainability and security. Environmental Research 201, 111527. DOI: 10.1016/J.ENVRES.2021.111527 DOI: https://doi.org/10.1016/j.envres.2021.111527
  • Brentan, B. M., Zanfei, A., Mazzoni, F., Marsili, V., Oberascher, M., Stelzl, A., Fuchs-Hanusch, D., 12 2023. Forecasting urban peak water demand based on climate indices and demographic trends. Water 2024, 16, 127. DOI: 10.3390/W16010127 DOI: https://doi.org/10.3390/w16010127
  • Donkor, E. A., Mazzuchi, T. A., Soyer, R., Roberson, J. A., 2 2014. Urban water demand forecasting: Review of methods and models. Journal of Water Resources Planning and Management 140, 146–159. DOI: 10.1061/(ASCE)WR.1943-5452.0000314 DOI: https://doi.org/10.1061/(ASCE)WR.1943-5452.0000314
  • Dabrowska, J., Orellana, A. E. M., Kilian, W., Moryl, A., Cielecka, N., Michałowska, K., Policht-Latawiec, A., Michalski, A., Bednarek, A., Włóka, A., 11 2023. Between flood and drought: How cities are facing water surplus and scarcity. Journal of Environmental Management 345, 118557, este es muy útil para contextualizar. DOI: 10.1016/J.JENVMAN.2023.118557 DOI: https://doi.org/10.1016/j.jenvman.2023.118557
  • Fong, B., Housh, M., Hong, G. Y., Wang, J. M., 1 2024. Pipeline management technologies for sustainable water supply in a smart city environment. Reference Module in Earth Systems and Environmental Sciences, 671–679. DOI: 10.1016/B978-0-323-90386-8.00055-3 DOI: https://doi.org/10.1016/B978-0-323-90386-8.00055-3
  • Groppo, G. D. S., Costa, M. A., Libânio, M., 12 2019. Predicting water demand: A review of the methods employed and future possibilities. Water Science and Technology: Water Supply 19, 2179–2198. DOI: 10.2166/WS.2019.122 DOI: https://doi.org/10.2166/ws.2019.122