Comparing of Generalized Linear Models, Random Forest and Gradient Boosting Trees in Estimation of Reference Crop Evapotranspiration (Case Study: The Sistan Plain)

To improve the accuracy of estimating reference crop evapotranspiration for the efficient management of water resources and the optimal design of irrigation scheduling, the drawback of the traditional FAO-56 Penman–Monteith method requiring complete meteorological input variables needs to be overcome. This study evaluates the effects of using five data splitting strategies and three different time lengths of input datasets on predicting ET0. The random forest (RF) and extreme gradient boosting (XGB) models coupled with a K-fold cross-validation approach were applied to accomplish this objective. The results showed that the accuracy of the RF (R2 = 0.862, RMSE = 0.528, MAE = 0.383, NSE = 0.854) was overall better than that of XGB (R2 = 0.867, RMSE = 0.517, MAE = 0.377, NSE = 0.860) in different input parameters. Both the RF and XGB models with the combination of Tmax, Tmin, and Rs as inputs provided better accuracy on daily ET0 estimation than the corresponding models with other input combinations. Among all the data splitting strategies, S5 (with a 9:1 proportion) showed the optimal performance. Compared with the length of 30 years, the estimation accuracy of the 50-year length with limited data was reduced, while the length of meteorological data of 10 years improved the accuracy in southern China. Nevertheless, the performance of the 10-year data was the worst among the three time spans when considering the independent test. Therefore, to improve the daily ET0 predicting performance of the tree-based models in humid regions of China, the random forest model with datasets of 30 years and the 9:1 data splitting strategy is recommended.

Download Full-text

Selecting Essential Factors for Predicting Reference Crop Evapotranspiration Through Tree-based Machine Learning and Bayesian Optimisation

10.21203/rs.3.rs-1133099/v1 ◽

2021 ◽

Author(s):

Long Zhao ◽

Xinbo Zhao ◽

Yi Shi ◽

Yuhang Wang ◽

Ningbo Cui ◽

...

Keyword(s):

Machine Learning ◽

North China ◽

Machine Learning Algorithms ◽

Influential Factor ◽

Superior Performance ◽

Gradient Boosting ◽

Crop Evapotranspiration ◽

Reference Crop Evapotranspiration ◽

Extreme Gradient Boosting ◽

Reference Crop

Abstract Reference crop evapotranspiration (ETO) is a basic component of the hydrological cycle, and its estimation is critical for agricultural water resource management and scheduling. In this study, three tree-based machine learning algorithms (random forest [RF], gradient boosting decision tree [GBDT], and extreme gradient boosting [XGBoost]) were adopted to determine the essential factors for ETO prediction. The tree-based models were optimised using the Bayesian optimisation (BO) algorithm, and they were compared with three standalone models in terms of daily ETO and monthly mean ETO estimation in North China, with different input combinations of essential variables. The results indicated that solar radiation (Rs) and air temperature (Ts), including the maximum, minimum, and average temperature, in daily ETO were the key parameters affecting model prediction accuracy. Rs was the most influential factor in the monthly average ETO model, followed by Ts. Both relative humidity (RH) and wind speed at 2 m (U2) had little impact on ETO prediction at different scales, although their importance differed. Compared with the GBDT and RF models, the XGBoost model exhibited the highest performance for daily ETO and monthly mean ETO estimation. The hybrid tree-based models with the BO algorithm outperformed the standalone tree-based models. Overall, compared with other inputs, the model with three inputs (Rs, Ts, and RH/U2) had the highest accuracy. The BO-XGBoost model exhibited superior performance in terms of the global performance index (GPI) for daily ETO and monthly mean ETO prediction and it is recommended as a more accurate model predicting daily ETO and monthly mean ETO in North China or areas with a similar climate.

Download Full-text

Terrain Segmentation of Greece Using the Spatial and Seasonal Variation of Reference Crop Evapotranspiration

Advances in Meteorology ◽

10.1155/2016/3092671 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Vassilis Aschonitis ◽

George Miliaresis ◽

Kleoniki Demertzi ◽

Dimitris Papamichail

Keyword(s):

Principal Components ◽

Cross Correlation ◽

Linear Models ◽

Spatiotemporal Variability ◽

General Linear ◽

Integrated Analysis ◽

Crop Evapotranspiration ◽

Reference Crop Evapotranspiration ◽

General Linear Models ◽

Reference Crop

The study presents a combination of techniques for integrated analysis of reference crop evapotranspiration (ETo) in GIS environment. The analysis is performed for Greece and includes the use of (a) ASCE-standardized Penman-Monteith method for the estimation of 50-year mean monthlyETo, (b) cross-correlation and principal components analysis for the analysis of the spatiotemporal variability ofETo, (c)K-means clustering for terrain segmentation to regions with similar temporal variability ofETo, and (d) general linear models for the description ofETobased on clusters attributes. Cross-correlation revealed a negative correlation ofETowith both elevation and latitude and a week positive correlation with longitude. The correlation betweenEToand elevation was maximized during the warm season, while the correlation with latitude was maximized during winter. The first two principal components accounted for the 97.9% of total variance of mean monthlyETo.K-means segmented Greece to 11 regions/clusters. The categorical factor of cluster number together with the parameters of elevation, latitude, and longitude described satisfactorily theETothrough general linear models verifying the robustness of the cluster analysis. This research effort can contribute to hydroclimatic studies and to environmental decision support in relation to water resources management in agriculture.

Download Full-text