Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China-Reference-Cited by-同舟云学术

Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China

Published:2018-12 Issue: Volume:263 Page:225-241
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Fan Junliang,Yue Wenjun,Wu Lifeng,Zhang Fucang,Cai Huanjie,Wang Xiukang,Lu Xianghui,Xiang Youzhen

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Jiangxi Natural Science Foundation of China

Scientific Startup Foundation for Doctors of Northwest A&F University

“111” Project

Publisher

Elsevier BV

Subject

Atmospheric Science,Agronomy and Crop Science,Global and Planetary Change,Forestry

Reference82 articles.

1. Extreme learning machines: a new approach for prediction of reference evapotranspiration;Abdullah;J. Hydrol.,2015

2. Comparative study of M5 model tree and artificial neural network in estimating reference evapotranspiration using MODIS products;Alipour;J. Climatol.,2014

3. Crop Evapotranspiration-guidelines for Computing Crop Water requirements-FAO Irrigation and Drainage Paper 56;Allen,1998

4. Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables;Antonopoulos;Comput. Electron. Agric.,2017

5. Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN);Baba;Hydrol. Res.,2013

Cited by 361 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effects of biochar amendment on greenhouse tomato quality, nutrient uptake and use efficiency under various irrigation and fertilization regimes;Scientia Horticulturae;2024-11

2. Optimal drip irrigation leaching amount and times enhance seed cotton yield and its stability by improving soil chemical environment and source-sink relationship;Field Crops Research;2024-10

3. Modeling ammonia concentration in swine building using biophysical data and machine learning algorithms;Computers and Electronics in Agriculture;2024-10

4. Determining effect of fertilization on reactive nitrogen losses through nitrate leaching and key influencing factors in Chinese agricultural systems;Agricultural Water Management;2024-10

5. Estimation of Daily Reference Evapotranspiration using Machine Learning and Deep Learning Techniques with Sparse Meteorological Data;2024-09-03