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As a measure of water quality, water turbidity might be a source of water pollution in drinking water resources. Henceforth, having a reliable tool for predicting turbidity values based on common water quantity/quality measured parameters is of great importance. In the present paper, the performance of the online sequential extreme learning machine (OS‐ELM) in predicting daily values of turbidity in Brandywine Creek, Pennsylvania, is evaluated. For this purpose, in addition to the developed OS‐ELM, several data‐driven models, that is, multilayer perceptron neural network (MLPANN), the classification and regression tree (CART), the group method of data handling (GMDH) and the response surface method (RSM) have been applied. The general findings of the study confirm the superiority of the OS‐ELM model over the other applied models so that the OS‐ELM improved the averaged RMSE of the predicted values 9.1, 11.7, 20.5 and 29.3% over the MLPANN, GMDH, RSM and CART models, respectively.

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This study challenges the use of three nature-inspired algorithms as learning frameworks of the adaptive-neuro-fuzzy inference system (ANFIS) machine learning model for short-term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography-based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R 2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R 2 ), root-mean-square error (RMSE), Nash-Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all-nature-inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS-PSO and ANFIS-BOA provide slightly better results than the other ANFIS models.