45 citations as recorded by crossref.

1. 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
2. Calibrating anomalies improves forecasting of daily reference crop evapotranspiration Q. Yang et al. 10.1016/j.jhydrol.2022.128009
3. Development of a Multilayer Deep Neural Network Model for Predicting Hourly River Water Temperature From Meteorological Data R. Abdi et al. 10.3389/fenvs.2021.738322
4. 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
5. Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia M. Ali et al. 10.1016/j.renene.2021.06.052
6. Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India A. Satpathi et al. 10.1007/s00704-024-04953-3
7. Forecasting of solar radiation using different machine learning approaches V. Demir & H. Citakoglu 10.1007/s00521-022-07841-x
8. Estimating daily reference evapotranspiration using a novel hybrid deep learning model L. Xing et al. 10.1016/j.jhydrol.2022.128567
9. Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece S. Dimitriadou & K. Nikolakopoulos 10.3390/w14132027
10. Parameter regionalization based on machine learning optimizes the estimation of reference evapotranspiration in data deficient area Z. Shu et al. 10.1016/j.scitotenv.2022.157034
11. Influence of Pre-Processing Algorithms on Surface Water TDS Estimation Using Artificial Intelligence Models: A Case Study of the Karoon River J. Panahi et al. 10.1007/s40996-022-00928-4
12. Estimation of reference evapotranspiration via machine learning algorithms in humid and semiarid environments in Khyber Pakhtunkhwa, Pakistan S. Gul et al. 10.1007/s13762-022-04334-1
13. Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye Ö. Coşkun & H. Citakoglu 10.1016/j.pce.2023.103418
14. 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
15. Design of an Apodized Fiber Bragg Grating Sensor for Sensitivity Analysis of Physical Parameters using Support Vector Machine H. Mandal & S. Sidhishwari 10.1007/s13538-024-01431-z
16. A Review on Evapotranspiration Estimation in Agricultural Water Management: Past, Present, and Future S. Wanniarachchi & R. Sarukkalige 10.3390/hydrology9070123
17. 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
18. AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models J. Bellido-Jiménez et al. 10.3390/agronomy12030656
19. 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
20. Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios P. Santos et al. 10.3390/agronomy13092366
21. 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
22. Development of the Statistical Errors Raster Toolbox with Six Automated Models for Raster Analysis in GIS Environments S. Dimitriadou & K. Nikolakopoulos 10.3390/rs14215446
23. Long Short-Term Memory Networks to Predict One-Step Ahead Reference Evapotranspiration in a Subtropical Climatic Zone D. Roy 10.1007/s40710-021-00512-4
24. 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
25. Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate A. Raza et al. 10.3390/w15213822
26. Deep learning approaches and interventions for futuristic engineering in agriculture S. Chakraborty et al. 10.1007/s00521-022-07744-x
27. Evapotranspiration Trends and Interactions in Light of the Anthropogenic Footprint and the Climate Crisis: A Review S. Dimitriadou & K. Nikolakopoulos 10.3390/hydrology8040163
28. Evapotranspiration estimation using SEBAL algorithm integrated with remote sensing and experimental methods N. Shamloo et al. 10.1080/17538947.2021.1962996
29. Annual Actual Evapotranspiration Estimation via GIS Models of Three Empirical Methods Employing Remotely Sensed Data for the Peloponnese, Greece, and Comparison with Annual MODIS ET and Pan Evaporation Measurements S. Dimitriadou & K. Nikolakopoulos 10.3390/ijgi10080522
30. A hybrid deep learning model based on feature capture of water level influencing factors and prediction error correction for water level prediction of cascade hydropower stations under multiple time scales X. Ma et al. 10.1016/j.jhydrol.2022.129044
31. Gas consumption demand forecasting with empirical wavelet transform based machine learning model: A case study M. AL‐Musaylh et al. 10.1002/er.6788
32. Comparison of machine learning techniques and spatial distribution of daily reference evapotranspiration in Türkiye D. Yildirim et al. 10.1007/s13201-023-01912-7
33. Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms R. Adnan et al. 10.1016/j.compag.2021.106541
34. 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
35. Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece S. Dimitriadou & K. Nikolakopoulos 10.3390/hydrology9070124
36. 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
37. Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems E. Uncuoglu et al. 10.1016/j.asoc.2022.109623
38. 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
39. Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration A. Elbeltagi et al. 10.1007/s13201-022-01667-7
40. Performance evaluation of soft computing techniques for forecasting daily reference evapotranspiration J. Rajput et al. 10.2166/wcc.2022.385
41. Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey H. Citakoglu 10.1007/s12517-021-08484-3
42. Evaluation of Data-driven Hybrid Machine Learning Algorithms for Modelling Daily Reference Evapotranspiration N. Kushwaha et al. 10.1080/07055900.2022.2087589
43. General and regional cross-station assessment of machine learning models for estimating reference evapotranspiration Y. Zouzou & H. Citakoglu 10.1007/s11600-022-00939-9
44. Reference evapotranspiration prediction using high-order response surface method B. Keshtegar et al. 10.1007/s00704-022-03954-4
45. Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir M. Idrees et al. 10.1007/s00477-021-01982-6