Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China

2021 ◽  
Vol 190 ◽  
pp. 106424
Author(s):  
Long Zhao ◽  
Xinbo Zhao ◽  
Hanmi Zhou ◽  
Xianlong Wang ◽  
Xuguang Xing
Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 15
Author(s):  
Zongjun Wu ◽  
Ningbo Cui ◽  
Bin Zhu ◽  
Long Zhao ◽  
Xiukang Wang ◽  
...  

Reference crop evapotranspiration (ET0) is an important indicator for precise regulation of crop water content, irrigation forecast formulation, and regional water resources management. The Hargreaves model (HG) is currently recognized as the simplest and most effective ET0 estimation model. To further improve the prediction accuracy of the HG model, this study is based on the data of 98 meteorological stations in southwest China (1961–2019), using artificial bee colony (ABC), differential evolution (DE) and particle swarm optimization (PSO) algorithms to calibrate the HG model globally. The standard ET0 value was calculated by FAO-56 Penman–Monteith (PM) model. We compare the calculation accuracy of 3 calibrated HG models and 4 empirical models commonly used (Hargreaves, Priestley–Taylor, Imark–Allen and Jensen–Hais). The main outcomes demonstrated that on a daily scale, the calibrated HG models (R2 range 0.74–0.98) are more accurate than 4 empirical models (R2 range 0.55–0.84), and ET0-PSO-HG has the best accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with average R2 of 0.83, 0.82 and 0.80, average RRMSE of 0.23 mm/d, 0.25 mm/d and 0.26 mm/d, average MAE of 0.52 mm/d, 0.53 mm/d and 0.57 mm/d, and average GPI of 0.17, 0.05, and 0.04, respectively; on a monthly scale, ET0-PSO-HG also has the highest accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with median R2 of 0.96, 0.95 and 0.94, median RRMSE of 0.16 mm/d, 0.17 mm/d and 0.18 mm/d respectively, median MAE of 0.46 mm/d, 0.50 mm/d, and 0.55 mm/d, median GPI of 1.12, 0.44 and 0.34, respectively. The calibrated HG models (relative error of less than 10.31%) are also better than the four empirical models (relative error greater than 16.60%). Overall, the PSO-HG model has the most accurate ET0 estimation on daily and monthly scales, and it can be suggested as the preferred model to predict ET0 in humid regions in southwest China regions.


2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Vassilis Aschonitis ◽  
George Miliaresis ◽  
Kleoniki Demertzi ◽  
Dimitris Papamichail

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.


1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.


Sign in / Sign up

Export Citation Format

Share Document