Bat algorithm optimised extreme learning machine (Bat-ELM)

Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat-ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (1) using air temperature (Ta) as input for predicting Tw, and (2) using Ta and the periodicity (i.e., day, month and year number). River Tw calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat-ELM accurately predicts the Tw and surpassed all other models with coefficient of correlation (R) values ranging within the limits of 0.973 to 0.981, and the Nash-Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat-ELM using the periodicity by enhancing its ability to estimate river Tw

Air temperature · Algorithm · Artificial neural network · Bat algorithm · Correlation coefficient · Extreme Learning Machine · Linear regression · Meteorology · Multilayer perceptron · Physics · Statistics · Artificial Intelligence · Computer Science · Hydrological Forecasting Using AI · Machine Learning and ELM · Mathematics · Water Quality Monitoring Technologies

• Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance

  CJ Willmott, C J Willmott et al.•Climate Research•2005

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