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Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales

Author

Listed:
  • Laís Coelho Teixeira

    (Universidade Federal do Rio Grande do Sul)

  • Priscila Pacheco Mariani

    (Universidade Federal do Rio Grande do Sul)

  • Olavo Correa Pedrollo

    (Universidade Federal do Rio Grande do Sul)

  • Nilza Maria Castro

    (Universidade Federal do Rio Grande do Sul)

  • Vanessa Sari

    (Universidade Federal de Santa Maria)

Abstract

The monitoring of hydro-sedimentological processes is important for environmental control but depends on resources that are not always available. The estimation of sedimentological variables with mathematical models is often limited by the scarcity of data for a single basin. This research experiments with the simulation of suspended sediment concentration (SSC) and turbidity (T) using a regional model, with data from agricultural basins of different scales within the same hydrographic region, using hourly precipitation as one of the predictive variables, aggregated through the exponentially weighted moving average (EWMA) of past rainfall, in artificial neural network (ANN) and fuzzy inference system (FIS) models. The data monitoring was performed from January 2013 to February 2020 in four watersheds within the same region in southern Brazil, with areas ranging from 1.3 to 524.3 km2. For the turbidity estimation, the FIS model, which also made use of the discharge (Q) and area (A) of each basin as inputs, performed best, with a Nash-Sutcliffe efficiency (NS) of 0.860 for the verification samples. Several FIS and ANN models performed very well for SSC prediction (with NSs ranging from 0.950 to 0.977) due to the EWMA variable, including an FIS model that uses only this variable (NS 0.952). The results allow us to conclude that it is possible, with few data for the individual basin and a regional empirical model, to estimate SSC and turbidity, provided the aggregation of hourly precipitation by the EWMA, as long as the basins have similar physical and climatic characteristics.

Suggested Citation

  • Laís Coelho Teixeira & Priscila Pacheco Mariani & Olavo Correa Pedrollo & Nilza Maria Castro & Vanessa Sari, 2020. "Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3709-3723, September.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:11:d:10.1007_s11269-020-02647-9
    DOI: 10.1007/s11269-020-02647-9
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    References listed on IDEAS

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    1. Saeed Azimi & Mehdi Azhdary Moghaddam, 2020. "Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1369-1405, March.
    2. Vanessa Sari & Nilza Maria Reis Castro & Olavo Correa Pedrollo, 2017. "Estimate of Suspended Sediment Concentration from Monitored Data of Turbidity and 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. 31(15), pages 4909-4923, December.
    3. Partha Majumder & T.I. Eldho, 2020. "Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 763-783, January.
    4. Meral Buyukyildiz & Serife Yurdagul Kumcu, 2017. "An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1343-1359, March.
    5. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
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    Cited by:

    1. Priscila Pacheco Mariani & Nilza Maria Reis Castro & Vanessa Sari & Taís Carine Schmitt & Olavo Correa Pedrollo, 2024. "Different Infiltration Methods for Swat Model Seasonal Calibration of Flow and Sediment Production," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 303-322, January.
    2. Yi Tang & Yang Pan & Lei Zhang & Hongchen Yi & Yiping Gu & Weihao Sun, 2023. "Efficient Monitoring of Total Suspended Matter in Urban Water Based on UAV Multi-spectral Images," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 2143-2160, March.

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