New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes-Reference-Cited by-同舟云学术

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New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes

Published:2023-11 Issue: Volume:155 Page:111030
ISSN:1470-160X
Container-title:Ecological Indicators
language:en
Short-container-title:Ecological Indicators

Author:

Ali Mumtaz,Jamei Mehdi,Prasad Ramendra,Karbasi Masoud,Xiang Yong,Cai Borui,Abdulla Shahab,Ahsan Farooque Aitazaz,Labban Abdulhaleem H.

Publisher

Elsevier BV

Subject

Ecology,Ecology, Evolution, Behavior and Systematics,General Decision Sciences

Reference77 articles.

1. Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting;Ali;Atmos. Res.,2018

2. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition;Ali;Renew. Sustain. Energy Rev.,2019

3. Complete Ensemble Empirical Mode Decomposition Hybridized with Random Forest and Kernel Ridge Regression Model for Monthly Rainfall Forecasts;Ali;J. Hydrol.,2020

4. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56;Allen;Fao, Rome,1998

5. Andersson, L. E., M. F. Aftab, F. Scibilia and L. Imsland (2017). Forecasting using multivariate empirical mode decomposition—Applied to iceberg drift forecast. 2017 IEEE Conference on Control Technology and Applications (CCTA), IEEE.

Cited by 2 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. Enhancing references evapotranspiration forecasting with teleconnection indices and advanced machine learning techniques;Applied Water Science;2024-09-14

2. Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting;Engineering Applications of Artificial Intelligence;2024-09

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