Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Abstract

Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites.

Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches

“…Similar results were obtained by using the same input combinations during the testing phase at all 10 stations, as indicated in Tables S1-S9. The results are in accordance with the findings of previous studies (dos Santos Farias et al 2020;Mehdizadeh et al 2021;Elbeltagi et al 2022a, b), which revealed that the use of all climatic variables in the models led to higher prediction accuracies.…”

Optimizing ETo Prediction in the Mahanadi Basin: A Comprehensive Evaluation of Machine Learning Models with Emphasis on ANFIS Performance

“…Wang et al, 2022)(Mehdizadeh et al, 2021)(Aghelpour et al, 2022)(Kadkhodazadeh et al, 2022)(Adnan et al, 2021). These investigations have demonstrated the effectiveness of combining AI techniques for the accurate prediction of ETo, which aligns with the ndings of the present study.…”

Are Supervised Learning Methods Suitable for Estimating Crop Water Consumption under Optimal and Deficit Irrigation?

“…This can be explained by the ability of selected models to autonomously solve complex and nonlinear problems by gathering datasets from various sources [70]. The models' performance improved with an increasing number of input variables [42]. The greatest positive impact on the models' performance was observed when the crop coefficient data were added to weather data availability (scenario 2).…”

“…Today, these interactions can be successfully described by modern mathematical tools, including the application of machine learning methods. In the last few years, various machine learning methods have been tested to estimate both reference and crop ET [35][36][37][38][39][40][41][42][43][44]. In particular, these methods have been developed to enhance the prediction accuracy for the estimation of ETo with limited data availability and ETc under optimal water supply.…”