Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting-Reference-Cited by-同舟云学术

Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting

Published:2023-12 Issue: Volume:215 Page:108387
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Troncoso-García A.R.^ORCID,Brito I.S.,Troncoso A.,Martínez-Álvarez F.

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference49 articles.

1. Parallel execution combinatorics with metaheuristics: Comparative study;Abdelhafez;Swarm Evol. Comput.,2020

2. Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage;Allen,1998

3. An update for the definition of reference evapotranspiration;Allen;ICID Bulletin,1994

4. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI;Arrieta;Inf. Fusion,2020

5. Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing;Carter;Int. J. Appl. Earth Obs. Geoinf.,2019

Cited by 6 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. Comparative analysis of hyperspectral Image reconstruction using deep learning for agricultural and biological applications;Results in Engineering;2024-09

2. A deep time-series water level prediction framework based on internal and external influencing factors: Targeting agricultural irrigation reservoirs;Computers and Electronics in Agriculture;2024-07

3. Dynamic land cover evapotranspiration model algorithm: DyLEMa;Computers and Electronics in Agriculture;2024-05

4. Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study;Journal of Environmental Management;2024-03

5. Research on Water Resource Modeling Based on Machine Learning Technologies;Water;2024-01-31