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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time

Antonio Madueño Luna1, Miriam López Lineros2, Javier Estévez Gualda3

  • 1Aerospace Engineering and Fluid Mechanical Department, University of Seville, 41013 Seville, Spain.

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Summary

This study evaluated gap-filling techniques for incomplete hydrometeorological data, finding cubic splines and multilayer perceptrons effective. An Internet of Things device was developed for real-time data pre-validation, improving water resource management.

Keywords:
ArduinoIoTRaspberry picubic splinesgap-fillingmultilayer perceptronradial basis functionsriver stage data

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Area of Science:

  • Environmental Science
  • Data Science
  • Engineering

Background:

  • Hydrometeorological data sets are often incomplete, impacting decision-making and analysis methods.
  • Climate change exacerbates extreme events, necessitating improved water resource management.
  • Accurate, real-time data estimation is crucial for effective water resource management.

Purpose of the Study:

  • To evaluate gap-filling techniques for river stage series.
  • To develop a real-time data pre-validation system using Internet of Things (IoT) technology.

Main Methods:

  • Comparison of gap-filling techniques: cubic splines (CS), radial basis function (RBF), and multilayer perceptron (MLP).
  • Development of an IoT device employing a dynamic seed non-linear autoregressive neural network (NARNN) for real-time pre-validation.
  • Testing of the IoT device with 4G remote communication for data transmission to a catchment basin process center (CPC).

Main Results:

  • Cubic splines and multilayer perceptrons demonstrated superior performance in filling data gaps for river stage series.
  • The developed IoT device successfully performed automatic, real-time data pre-validation.
  • The system effectively transmitted validated data to the CPC via 4G technology.

Conclusions:

  • Cubic splines and MLPs are recommended for hydrometeorological data gap-filling, especially on resource-constrained platforms.
  • The developed IoT device offers a viable solution for real-time data quality control in water resource management.
  • The integration of IoT and advanced algorithms enhances the reliability of hydrometeorological data for climate change adaptation.