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Optimized Design of Neural Networks for a River Water Level Prediction System.

Miriam López Lineros1, Antonio Madueño Luna2, Pedro M Ferreira3

  • 1Design Engineering Department, University of Seville, 41013 Seville, Spain.

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|October 13, 2021
PubMed
Summary

This study introduces a Multi-Objective Genetic Algorithm (MOGA) framework for designing Artificial Neural Network (ANN) models to predict river water levels. The MOGA framework efficiently creates accurate, low-complexity models for forecasting unseen river flow data.

Keywords:
Multi-Objective Genetic Algorithmartificial neural networksriver stage data

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Area of Science:

  • Hydrology and Water Resource Management
  • Computational Intelligence
  • Artificial Intelligence in Environmental Science

Background:

  • Accurate river water level prediction is crucial for effective water resource management and flood control.
  • Traditional methods for designing Artificial Neural Network (ANN) models can be complex and time-consuming.
  • Developing robust models that perform well on unseen data is a significant challenge in hydrological forecasting.

Purpose of the Study:

  • To present a near-automatic Multi-Objective Genetic Algorithm (MOGA) framework for designing Artificial Neural Network (ANN) models.
  • To develop 1-step-ahead prediction models for river water levels using the MOGA framework.
  • To evaluate the performance of MOGA-designed ANN models on unseen river flow data.

Main Methods:

  • Utilized a Multi-Objective Genetic Algorithm (MOGA) to automate the design of Artificial Neural Network (ANN) models.
  • Implemented a data partitioning strategy within the MOGA framework for model training and validation.
  • Determined optimal ANN topology and input variables for 1-step-ahead river water level prediction.

Main Results:

  • The MOGA framework successfully designed low-complexity ANN models for river water level prediction.
  • Models demonstrated excellent performance on unseen data, achieving a Root Mean Square Error (RMSE) of 2.5 × 10-3.
  • The MOGA approach yielded results comparable or superior to alternative model design methods.

Conclusions:

  • The MOGA framework offers an efficient and effective approach for designing ANN models in hydrological applications.
  • The proposed method provides accurate and robust 1-step-ahead river water level predictions.
  • This framework facilitates the development of high-performing hydrological models with minimal manual intervention.