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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with

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Summary

This study introduces a new water quality forecasting model, WER-GBO, for accurate monthly sodium prediction in Iran's Maroon River. The novel ensemble approach significantly improves prediction accuracy compared to existing methods.

Keywords:
Bayesian linear regressionGradient-based optimizationWater qualityWavelet transformWeighted exponential regression

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Intelligence

Background:

  • Water quality forecasting is vital for environmental health and economic development.
  • Accurate time series prediction aids in timely pollution warnings and water resource management decisions.

Purpose of the Study:

  • To develop a novel ensemble data intelligence model, Weighted Exponential Regression and Hybridized by Gradient-Based Optimization (WER-GBO).
  • To achieve meticulous monthly sodium (Na+) prediction in the Maroon River, Iran.
  • To enhance water quality forecasting accuracy using advanced hybrid modeling techniques.

Main Methods:

  • Ensemble of four data intelligence models: Adaptive Neuro-Fuzzy Inference System (ANFIS), Least Square Support Vector Regression (LSSVM), Bayesian Linear Regression (BLR), and Response Surface Regression (RSR).
  • Integration of a Cauchy weighted function with an exponential-based regression model.
  • Optimization of the model using the Gradient-Based Optimization (GBO) algorithm.

Main Results:

  • The proposed WER-GBO model demonstrated superior accuracy in Na+ prediction compared to individual models (ANFIS, LSSVM, BLR, RSR).
  • Statistical metrics showed high performance for WER-GBO: R=0.9712, RMSE=0.639, KGE=0.948.
  • Graphical assessments confirmed the robustness and reliability of the WER-GBO model.

Conclusions:

  • The WER-GBO model is a highly accurate and reliable technique for forecasting water quality parameters, specifically sodium levels.
  • This advanced ensemble approach offers a constructive solution for improving water resource management and environmental monitoring.
  • The study highlights the potential of hybridized data intelligence models in addressing complex environmental prediction challenges.