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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm.

Mohammad Ehteram1, Fatemeh Panahi2, Ali Najah Ahmed3

  • 1Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran.

Environmental Science and Pollution Research International
|September 16, 2021
PubMed
Summary

This study enhanced daily evaporation prediction using a Multilayer Perceptron (MLP) model coupled with the Multi-Objective Salp Swarm Algorithm (MOSSA). The MLP-MOSSA demonstrated superior accuracy and reduced uncertainty compared to other methods.

Keywords:
EvaporationMLPMulti-objective optimization algorithmsPareto front

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Artificial Intelligence in Engineering

Background:

  • Accurate evaporation prediction is vital for agriculture and water engineering.
  • Traditional models often require enhancement for precise daily evaporation forecasting.
  • Artificial Neural Networks, specifically Multilayer Perceptrons (MLP), offer a robust framework for complex predictions.

Purpose of the Study:

  • To improve the accuracy of daily evaporation prediction using an enhanced Multilayer Perceptron (MLP) model.
  • To evaluate the performance of coupling MLP with multi-objective optimization algorithms: Multi-Objective Salp Swarm Algorithm (MOSSA), Multi-Objective Crow Algorithm (MOCA), and Multi-Objective Particle Swarm Optimization (MOPSO).
  • To determine the optimal model parameters, input combinations, and activation functions for evaporation prediction.

Main Methods:

  • Utilized the Multilayer Perceptron (MLP) model for daily evaporation prediction.
  • Coupled MLP with three multi-objective algorithms (MOSSA, MOCA, MOPSO) to optimize model parameters, inputs, and activation functions.
  • Evaluated model performance using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Percent Bias (PBIAS), and Nash-Sutcliffe Efficiency (NSE) across three Malaysian stations.

Main Results:

  • The MLP model coupled with MOSSA (MLP-MOSSA) consistently outperformed MLP-MOCA, MLP-MOPSO, and the standalone MLP model in accuracy across all tested stations.
  • MLP-MOSSA achieved significant reductions in RMSE (up to 35%) and MAE (up to 26%) compared to other models.
  • Uncertainty analysis revealed that MLP-MOSSA exhibited the lowest prediction uncertainty, with input uncertainty being lower than parameter uncertainty.

Conclusions:

  • The MLP-MOSSA approach provides a highly efficient and accurate method for daily evaporation prediction.
  • Multi-objective optimization algorithms, particularly MOSSA, are effective in enhancing the performance of MLP models for hydrological forecasting.
  • The findings support the application of advanced AI techniques for improved water resource management and agricultural planning.