Dissolved oxygen modelling of Yamuna River using different ANFIS models

Dissolved oxygen is one of the prime parameters for assessing the water quality of any stream. Thus, the accurate estimation of dissolved oxygen is necessary to evolve measures for maintaining the riverine ecosystem and designing the appropriate water quality improvement plans. Machine learning techniques are becoming valuable tools for the prediction and simulation of water quality parameters. A study has been performed in the Delhi stretch of Yamuna River, India, and physiochemical parameters were examined for five years to simulate the dissolved oxygen using various machine learning techniques. Simulation and prediction competencies of adaptive neuro fuzzy inference system - grid partitioning (ANFIS-GP) and subtractive clustering (ANFIS-SC) were performed on high dimensional river characteristics. Four different models (M1, M2, M3 and M4) were developed using different combination of input parameters to predict dissolved oxygen. Results obtained from the models were evaluated using root mean square error (RMSE) and coefficient of determination (R-2) to identify the appropriate combination of parameters to simulate the dissolved oxygen. Results suggest that both types of ANFIS models work adequately and accurately predict the DO; however, ANFIS-GP outperforms the ANFIS-SC. M4 generated R-2 of 0.953 from ANFIS-GP compared to 0.911 from ANFIS-SC.

Automated summary

Dissolved oxygen is a key parameter in assessing river water quality, and machine learning techniques are valuable tools for predicting and simulating water quality parameters. A study in the Delhi stretch of Yamuna River showed that the ANFIS-GP model outperformed the ANFIS-SC model in accurately predicting dissolved oxygen levels.