Huanjie Cai

TL;DR: In this paper, a field experiment was conducted for winter wheat (Triticum aestivum L.) during the period 1995-1998 to evaluate the effects of limited irrigation on yield and water use efficiency.

TL;DR: In this paper, the authors evaluated the potential of tree-based assemble algorithms (i.e., random forest, M5 model tree, gradient boosting decision tree and extreme gradient boosting) for estimating daily evapotranspiration (ET0) with limited meteorological data using a K-fold cross-validation method.

TL;DR: In this article, the performance of 12 empirical model forms and 12 machine learning algorithms for estimating daily solar radiation (Rs) were further evaluated in different climatic zones of China as a case study, i.e., the temperate continental zone (TCZ), temperate monsoon zone (TMZ), mountain plateau zone (MPZ) and (sub)tropical monsoon zones (SMZ).

TL;DR: In this paper, the accuracy and suitability of 72 existing and developed empirical models in nine categories are evaluated for estimating diffuse horizontal solar radiation in different climatic regions of China, i.e., the arid desert of northwest China, the Qinghai-Tibetan Plateau (QTP), the (semi-)humid cold-temperate northeast China (NEC), the semi-humid north China (NC) and the humid (sub-)tropical central and south China (CSC).

TL;DR: Wang et al. as mentioned in this paper used Support Vector Machine (SVM) to predict global and diffuse solar radiation (Rs and Rd) in China's capital city of Beijing, using daily meteorological and air pollution data from January 2014 to December 2016 for testing, and the results demonstrate the importance of appropriate selection of air pollution inputs to improve the accuracy of solar radiation prediction.