Improving reference evapotranspiration estimation using novel inter-model ensemble approaches
出版年份 2021 全文链接
标题
Improving reference evapotranspiration estimation using novel inter-model ensemble approaches
作者
关键词
Inter-model ensemble, Bootstrap aggregating, Bayesian model averaging, Non-linear neural ensemble, Reference evapotranspiration
出版物
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 187, Issue -, Pages 106227
出版商
Elsevier BV
发表日期
2021-06-03
DOI
10.1016/j.compag.2021.106227
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review
- (2020) Min Yan Chia et al. Agronomy-Basel
- Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review
- (2020) Keyu Xiang et al. AGRICULTURAL WATER MANAGEMENT
- Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm
- (2020) Babak Mohammadi et al. AGRICULTURAL WATER MANAGEMENT
- ANFIS Modeling with ICA, BBO, TLBO, and IWO Optimization Algorithms and Sensitivity Analysis for Predicting Daily Reference Evapotranspiration
- (2020) Maryam Zeinolabedini Rezaabad et al. JOURNAL OF HYDROLOGIC ENGINEERING
- Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine
- (2020) Min Yan Chia et al. AGRICULTURAL WATER MANAGEMENT
- Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods
- (2020) Zhijun Chen et al. JOURNAL OF HYDROLOGY
- Drought forecasting: A review of modelling approaches 2007–2017
- (2019) K. F. Fung et al. Journal of Water and Climate Change
- Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach
- (2019) Lucas Borges Ferreira et al. JOURNAL OF HYDROLOGY
- Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models
- (2019) Huaiwei Sun et al. Atmosphere
- Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing
- (2019) Corinne Carter et al. International Journal of Applied Earth Observation and Geoinformation
- Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
- (2019) Guomin Huang et al. JOURNAL OF HYDROLOGY
- An improved model based on the support vector machine and cuckoo algorithm for simulating reference evapotranspiration
- (2019) Mohammad Ehteram et al. PLoS One
- Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data
- (2019) Matheus Mendes Reis et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches
- (2019) Yazid Tikhamarine et al. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES
- Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements
- (2019) Vahid Nourani et al. JOURNAL OF HYDROLOGY
- Combining generalized complementary relationship models with the Bayesian Model Averaging method to estimate actual evapotranspiration over China
- (2019) Yuefeng Hao et al. AGRICULTURAL AND FOREST METEOROLOGY
- Multi-step ahead modeling of reference evapotranspiration using a multi-model approach
- (2019) Vahid Nourani et al. JOURNAL OF HYDROLOGY
- Charting the water footprint for Malaysian crude palm oil
- (2018) Vijaya Subramaniam et al. JOURNAL OF CLEANER PRODUCTION
- MALAYSIA: 100 YEARS OF RESILIENT PALM OIL ECONOMIC PERFORMANCE
- (2018) BALU NAMBIAPPAN Journal of Oil Palm Research
- Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China
- (2018) Junliang Fan et al. AGRICULTURAL AND FOREST METEOROLOGY
- Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction
- (2016) A. Belayneh et al. ATMOSPHERIC RESEARCH
- Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging
- (2016) Gaofeng Zhu et al. HYDROLOGICAL PROCESSES
- Modifying Hargreaves–Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey
- (2016) Murat Cobaner et al. HYDROLOGY RESEARCH
- Estimating Evapotranspiration Using an Extreme Learning Machine Model: Case Study in North Bihar, India
- (2016) Deepak Kumar et al. JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING
- Estimating Evapotranspiration Using an Extreme Learning Machine Model: Case Study in North Bihar, India
- (2016) Deepak Kumar et al. JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING
- Modifying Hargreaves–Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey
- (2016) Murat Cobaner et al. Hydrology Research
- Extreme Learning Machines: A new approach for prediction of reference evapotranspiration
- (2015) Shafika Sultan Abdullah et al. JOURNAL OF HYDROLOGY
- Using Bayesian model averaging to estimate terrestrial evapotranspiration in China
- (2015) Yang Chen et al. JOURNAL OF HYDROLOGY
- Review and statistical analysis of different global solar radiation sunshine models
- (2015) Milan Despotovic et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
- (2011) M. Galar et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now