Hybrid COOT-ANN: a novel optimization algorithm for prediction of daily crop reference evapotranspiration in Australia

The present study evaluates the capability of a novel optimization method in modeling daily crop reference evapotranspiration (ETo), a critical issue in water resource management. A hybrid predictive model based on the artificial neural network (ANN) algorithm that is embedded within the COOT method (COOT bird natural life model-artificial neural network (COOT-ANN)) is developed and evaluated for its suitability for the prediction of daily ETo at seven meteorological stations in different states of Australia. Accordingly, a daily statistical period of 12 years (01-01-2010 to 31-12-2021) for climatic data of maximum temperature, minimum temperature, and ETo were collected. The results are evaluated using six performance criteria metrics: correlation coefficient (R), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), RMSE-observation standard deviation ratio (RSR), Scatter Index (SI), and mean absolute error (MAE) along with the Taylor diagrams. The performance of the COOT-ANN model was compared with those of the conventional ANN model. The results showed that the COOT-ANN hybrid model outperforms the ANN model at all seven stations by 0.803%, 4.127%, 3.359%, 4.072%, 4.148%, and 3.665% based on the average values of the R, RMSE, NSE, RSR, SI, and MAE criteria, respectively. So, this study provides an innovative method for prediction in agricultural and water resource studies.