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Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm.

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Accurate water quality assessment requires improved biochemical oxygen demand (BOD) prediction. Hybrid ANFIS-MPA models offer a superior, faster alternative to traditional methods for predicting BOD levels.

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Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Computational Intelligence

Background:

  • Biochemical oxygen demand (BOD) is a critical water quality indicator.
  • Traditional BOD measurement is time-consuming and prone to inaccuracies.
  • Developing accurate predictive models is essential for efficient water quality assessment.

Purpose of the Study:

  • To evaluate hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) models for predicting BOD.
  • To compare the performance of ANFIS integrated with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Marine Predators Algorithm (MPA).
  • To determine the optimal input combination for BOD prediction using Multivariate Adaptive Regression Spline (MARS).

Main Methods:

  • Utilized four hybrid ANFIS models: ANFIS-GA, ANFIS-PSO, ANFIS-SCA, and ANFIS-MPA.
  • Input variables included pH, dissolved oxygen (DO), electrical conductivity (EC), water temperature (WT), suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (T-P).
  • Applied MARS to identify the most effective input parameters for BOD prediction.

Main Results:

  • The ANFIS-MPA model demonstrated the best performance in BOD prediction.
  • ANFIS-MPA achieved the lowest root mean square error (RMSE) and mean absolute error (MAE), and the highest determination coefficient (R²).
  • ANFIS-MPA significantly improved RMSE compared to other models, especially for Gyeongan Station (up to 33% improvement).

Conclusions:

  • Hybrid ANFIS models, particularly ANFIS-MPA, provide a highly accurate and efficient method for predicting BOD.
  • The findings suggest ANFIS-MPA is a viable alternative to traditional BOD testing for water quality assessment.
  • Optimizing input variables using MARS enhances the predictive accuracy of the developed models.