Short-term daily forecasting of crop evapotranspiration of rice using public weather forecasts

Evaluation of Empirical Equations and Machine Learning Models for Daily Reference Evapotranspiration Prediction Using Public Weather Forecasts

“…At the three sites, the mean MAE and RMSE of T max ranged from 3.62-4.12 • C and 4.67-5.24 • C, respectively, while the mean R values of T min and T max were higher than 0.92 and 0.85, respectively. The accuracy of the T min forecasts at all three sites was higher than that of the T max forecasts, and the T min and T max prediction performance decreased with increasing forecast period, which is consistent with most previous studies in China [9][10][11]20,21,23,24,42,58,63]. In addition, the mean RM values of T min and T max ranged from 1.00-1.08 and 0.98-1.01, respectively, indicating that T min was slightly overestimated at the three sites, T max was slightly overestimated at GY, and T max was slightly underestimated at YC and TX.…”

“…The daily ET o prediction performance indicators of the three empirical equations and four machine learning models with a lead time of 1-7 days at the three sites are shown in Figures 8 and 9, respectively. First, the daily ET o prediction performance of the seven models at YC, TX, and GY decreased with increasing lead time, which is due to the decrease in the performance of the public weather forecast variables with increasing lead time, which is consistent with previous research results [1,2,[9][10][11]17,20,23,42,58,63]. Second, the RM values of the 7 models at YC ranged from 0.97 to 1.22, among which the XGBoost model slightly underestimated (2.04-2.96%) the daily ET o , the PMF equation overestimated (20.74-22.03%) the daily ET o , and the other models slightly overestimated (0.24-10.68%) the daily ET o .…”

“…Where no actual solar radiation data are available and no calibration is conducted to obtain improved a s and b s parameters, a s = 0.25 and b s = 0.50 are recommended [11,18,20,36]. The 1985 HS equation has been widely used in ET o estimation/prediction [9,11,22,23,[37][38][39][40][43][44][45][46][47][48][49]. The HS equation can be expressed as Equation (12), which has been applied in practice with corrections for parameters C and E [9,11,50,51]:…”

“…Given that temperature is the most accurate and only quantitative variable in public weather forecasts, many studies have used only temperature forecast data retrieved from public weather forecasts to predict ET o . Based on temperature data retrieved from public weather forecasts, the daily ET o has been predicted using locally calibrated versions of the HS equation [9,22,23], the daily ET o with a lead time of 1-7 days has been predicted using the monthly calibrated Blaney-Criddle (BC) model [24], and four ANN models (linear regression (LR), probabilistic neural network (PNN), MLP, and generalized feedforward (GFF) models) [10], and the GEP algorithm [2] have been used to predict the daily ET o with a lead time of 1-7 days at Gaoyou Station in Jiangsu Province of China. The above studies based on the adoption of public weather forecast models for daily ET o prediction have achieved reasonable results, but there exists no evaluation model to determine the seasonal variation in the daily ET o prediction performance, and there is a lack of comparative evaluation of machine learning models in daily ET o prediction based on different input combinations of weather variables retrieved from public weather forecasts.…”