Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling

Abstract Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature ( T w ) 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 ( T a ) as input for predicting T w , and (2) using T a and the periodicity (i.e., day, month and year number). River T w calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat‐ELM accurately predicts the T w 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 T w .