Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-603-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-603-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation
Mohammad Taghi Sattari
CORRESPONDING AUTHOR
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 51666, Iran
Institute of Research and Development, Duy Tan University, Danang
550000, Vietnam
Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey
Halit Apaydin
Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkey
Shahab S. Band
CORRESPONDING AUTHOR
Future Technology Research Center, National Yunlin University of
Science and Technology, Douliou, Yunlin 64002, Taiwan
Amir Mosavi
Faculty of Civil Engineering, Technische Universität Dresden,
01069 Dresden, Germany
John von Neumann Faculty of Informatics, Obuda University, 1034
Budapest, Hungary
School of Economics and Business, Norwegian University of Life
Sciences, 1430 Ås, Norway
Ramendra Prasad
Department of Science, School of Science and Technology, The
University of Fiji, Lautoka, Fiji
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- Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach D. Roy et al. 10.1007/s11269-021-03009-9
- Calibrating anomalies improves forecasting of daily reference crop evapotranspiration Q. Yang et al. 10.1016/j.jhydrol.2022.128009
- Enhancing the accuracy of wind power projections under climate change using geospatial machine learning models S. Moradian et al. 10.1016/j.egyr.2024.09.007
- Forecasting of solar radiation using different machine learning approaches V. Demir & H. Citakoglu 10.1007/s00521-022-07841-x
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- Groundwater salinization risk assessment using combined artificial intelligence models O. Dhaoui et al. 10.1007/s11356-024-33469-6
- Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya S. Ghosh et al. 10.1007/s00382-023-06893-6
- A regional machine learning method to outperform temperature-based reference evapotranspiration estimations in Southern Spain J. Bellido-Jiménez et al. 10.1016/j.agwat.2022.107955
- Reference Evapotranspiration (ETo) Methods Implemented as ArcMap Models with Remote-Sensed and Ground-Based Inputs, Examined along with MODIS ET, for Peloponnese, Greece S. Dimitriadou & K. Nikolakopoulos 10.3390/ijgi10060390
- Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions H. Apaydin et al. 10.1016/j.jhydrol.2021.126506
- Evaluation of bio-inspired optimization algorithms hybrid with artificial neural network for reference crop evapotranspiration estimation L. Gao et al. 10.1016/j.compag.2021.106466
- Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate A. Raza et al. 10.3390/w15213822
- Deep learning approaches and interventions for futuristic engineering in agriculture S. Chakraborty et al. 10.1007/s00521-022-07744-x
- Evapotranspiration Trends and Interactions in Light of the Anthropogenic Footprint and the Climate Crisis: A Review S. Dimitriadou & K. Nikolakopoulos 10.3390/hydrology8040163
- Evapotranspiration estimation using SEBAL algorithm integrated with remote sensing and experimental methods N. Shamloo et al. 10.1080/17538947.2021.1962996
- Gas consumption demand forecasting with empirical wavelet transform based machine learning model: A case study M. AL‐Musaylh et al. 10.1002/er.6788
- Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman–Monteith Equation in a Highly Advective Environment S. Kiraga et al. 10.3390/w16010012
- Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches J. T R et al. 10.1007/s11269-022-03399-4
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- Evaluation of Data-driven Hybrid Machine Learning Algorithms for Modelling Daily Reference Evapotranspiration N. Kushwaha et al. 10.1080/07055900.2022.2087589
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- Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions A. Elbeltagi et al. 10.1007/s00477-022-02196-0
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- Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States M. Valipour et al. 10.1016/j.agwat.2023.108311
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Latest update: 05 Dec 2024
Short summary
The aim of study is to estimate the reference evapotranspiration (ET0) amount with artificial intelligence using minimum meteorological parameters in the Corum region, which is an agricultural center of Turkey. Kernel-based GPR and SVR and BFGS-ANN and LSTM models were used to estimate ET0 amounts in 10 different combinations. The results show that all four methods used predicted ET0 amounts at acceptable accuracy and error levels. The BFGS-ANN model showed higher success than the others.
The aim of study is to estimate the reference evapotranspiration (ET0) amount with artificial...