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A generalized linear stochastic model for lake level prediction.

Mohammad Zeynoddin1, Hossein Bonakdari1, Isa Ebtehaj1

  • 1Department of Soils and Agri-Food Engineering, Laval University, Québec G1V0A6, Canada.

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Summary

A generalized linear stochastic model accurately forecasts Urmia Lake

Keywords:
Pre-processingSpectral analysisStandardizationStochastic modelUrmia lake levelWater resources

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Area of Science:

  • Environmental Science
  • Hydrology
  • Data Science

Background:

  • Endorheic lakes, particularly saline ones, are sensitive environmental indicators.
  • Monitoring and modeling lake levels are crucial for environmental management.
  • Urmia Lake, the sixth-largest saltwater lake, requires accurate water level forecasting.

Purpose of the Study:

  • To propose and evaluate a generalized linear stochastic model (GLSM) for forecasting Urmia Lake's water levels.
  • To compare the GLSM's performance against various artificial intelligence (AI) models.
  • To assess the effectiveness of different data preprocessing techniques.

Main Methods:

  • Developed a generalized linear stochastic model (GLSM).
  • Implemented three data preprocessing approaches: differencing, de-trending with standardization and spectral analysis, and the latter combined with normalization transform.
  • Compared GLSM with Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Multilayer Perceptron (MLP), Gene Expression Programming (GEP), Support Vector Machine with Firefly algorithm (SVM-FFA), and Artificial Neural Networks (ANN).

Main Results:

  • The GLSM significantly outperformed all AI models in forecasting weekly water levels, achieving an R² of 99.957% and RMSE of 2.121%.
  • For monthly forecasting, the GLSM also demonstrated superior performance with an R² of 99.517% and RMSE of 6.91%.
  • The study highlighted the effectiveness of appropriate data preprocessing in enhancing model accuracy.

Conclusions:

  • The generalized linear stochastic model (GLSM) provides a precise and reliable method for forecasting endorheic lake water levels.
  • GLSM offers a viable and effective alternative to complex AI models for hydrological time series analysis.
  • Accurate water level forecasting is essential for the sustainable management of sensitive saline lakes like Urmia Lake.