Predicting dissolved oxygen dynamics in the Bouregreg Estuary (Morocco) using ANN and ANFIS: Role of estuarine mixing and environmental parameters

Dissolved oxygen (DO) is a critical indicator of estuarine ecosystem health, influenced by complex physicochemical interactions. This study employs Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict DO concentrations in the Bouregreg Estuary, Morocco, using salinity, temperature, pH, and transparency data collected on 10/11/2021, 20/03/2022, and 02/07/2022 at three stations during high and low tides. The dataset includes DO concentrations (5.5–11.8 mg/L), salinity (19.4–35.8 ppt), temperature (17–23 °C), pH (7.0–8.7), and transparency (16–21 cm). Three ANN models (Back Propagation Neural Network [BPNN], Generalized Regression Neural Network [GRNN], Recurrent Neural Network [RNN]), and four ANFIS models (with triangular, trapezoidal, Gaussian, and Generalized Bell membership functions) were developed. The ANFIS model with Gaussian membership function (ANFIS4) achieved the highest performance in Scenario II (R2 = 0.9500, RMSE = 0.2800 mg/L, and PBIAS = −1.2000%). Temperature and salinity were key predictors, with salinity capturing estuarine mixing dynamics. Comprehensive visualizations of model validation, spatial-temporal variations, and parameter relationships enhance the understanding of DO dynamics. These findings provide a robust framework for estuarine management and highlight the role of tidal mixing in oxygen distribution.