53 citations as recorded by crossref.

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6. Groundwater salinization risk assessment using combined artificial intelligence models O. Dhaoui et al. 10.1007/s11356-024-33469-6
7. 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
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14. Evapotranspiration Trends and Interactions in Light of the Anthropogenic Footprint and the Climate Crisis: A Review S. Dimitriadou & K. Nikolakopoulos 10.3390/hydrology8040163
15. Evapotranspiration estimation using SEBAL algorithm integrated with remote sensing and experimental methods N. Shamloo et al. 10.1080/17538947.2021.1962996
16. Gas consumption demand forecasting with empirical wavelet transform based machine learning model: A case study M. AL‐Musaylh et al. 10.1002/er.6788
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18. 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
19. Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey H. Citakoglu 10.1007/s12517-021-08484-3
20. Evaluation of Data-driven Hybrid Machine Learning Algorithms for Modelling Daily Reference Evapotranspiration N. Kushwaha et al. 10.1080/07055900.2022.2087589
21. Enhanced TDS Modeling Using an AI Framework Integrating Grey Wolf Optimization with Kernel Extreme Learning Machine M. Sayadi et al. 10.3390/w16192818
22. 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
23. 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
24. A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
25. 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
26. Hybrid soft computing-based predictive models for shear strength of exterior reinforced concrete beam-column joints N. Safaeian Hamzehkolaei & M. Barkhordari 10.1007/s41939-024-00608-y
27. 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
28. Application of artificial neural networks to the modeling of rain-runoff in the Chancay Lambayeque river basin L. Ordoñez et al. 10.24850/j-tyca-2024-06-03
29. Estimating daily reference evapotranspiration using a novel hybrid deep learning model L. Xing et al. 10.1016/j.jhydrol.2022.128567
30. 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
31. 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
32. 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
33. 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
34. Improving actual evapotranspiration estimates through an integrated remote sensing and cutting-edge machine learning approach R. dos Santos et al. 10.1016/j.compag.2024.109258
35. 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
36. A Review on Evapotranspiration Estimation in Agricultural Water Management: Past, Present, and Future S. Wanniarachchi & R. Sarukkalige 10.3390/hydrology9070123
37. 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
38. Machine Learning and Conventional Methods for Reference Evapotranspiration Estimation Using Limited-Climatic-Data Scenarios P. Santos et al. 10.3390/agronomy13092366
39. Development of the Statistical Errors Raster Toolbox with Six Automated Models for Raster Analysis in GIS Environments S. Dimitriadou & K. Nikolakopoulos 10.3390/rs14215446
40. 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
41. Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models S. Yong et al. 10.1007/s11269-024-03860-6
42. 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
43. 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
44. Comparison of machine learning techniques and spatial distribution of daily reference evapotranspiration in Türkiye D. Yildirim et al. 10.1007/s13201-023-01912-7
45. Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms R. Adnan et al. 10.1016/j.compag.2021.106541
46. 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
47. Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece S. Dimitriadou & K. Nikolakopoulos 10.3390/hydrology9070124
48. 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
49. Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration A. Elbeltagi et al. 10.1007/s13201-022-01667-7
50. Performance evaluation of soft computing techniques for forecasting daily reference evapotranspiration J. Rajput et al. 10.2166/wcc.2022.385
51. General and regional cross-station assessment of machine learning models for estimating reference evapotranspiration Y. Zouzou & H. Citakoglu 10.1007/s11600-022-00939-9
52. Reference evapotranspiration prediction using high-order response surface method B. Keshtegar et al. 10.1007/s00704-022-03954-4
53. 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