Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods

[1]  H. Citakoglu,et al.  General and regional cross-station assessment of machine learning models for estimating reference evapotranspiration , 2022, Acta Geophysica.

[2]  E. Uncuoğlu,et al.  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 , 2022, Appl. Soft Comput..

[3]  Huaiwei Sun,et al.  Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model , 2022, Plants.

[4]  Hülya Bayrak,et al.  Tamamlanmış Blok ve Tamamen Rasgele Tasarımdan Oluşan Bir Karma Tasarımda Genel Alternatif için Önerilen Testler , 2022, Afyon Kocatepe University Journal of Sciences and Engineering.

[5]  Jun-Seok Oh,et al.  Transportation Mode Detection by Using Smartphones and Smartwatches with Machine Learning , 2022, KSCE Journal of Civil Engineering.

[6]  Muhammed A. Hassan,et al.  Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions , 2022, Environmental Science and Pollution Research.

[7]  Noman Ali Buttar,et al.  Development of Monthly Reference Evapotranspiration Machine Learning Models and Mapping of Pakistan—A Comparative Study , 2022, Water.

[8]  N. Al‐Ansari,et al.  Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration , 2022, Applied Water Science.

[9]  A. Sahoo,et al.  Imputation of missing precipitation data using KNN, SOM, RF, and FNN , 2022, Soft Computing.

[10]  Ahmad S.,et al.  Developing a Prototype Piezoelectric Wafer-Box for Optimal Energy Harvesting , 2022, Journal of Civil Engineering and Architecture.

[11]  Tonglin Fu,et al.  A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland , 2021, Journal of Hydrology.

[12]  G. Rodrigues,et al.  A Simple Application for Computing Reference Evapotranspiration with Various Levels of Data Availability—ETo Tool , 2021, Agronomy.

[13]  Junliang Fan,et al.  Medium-range forecasting of daily reference evapotranspiration across China using numerical weather prediction outputs downscaled by extreme gradient boosting , 2021 .

[14]  H. Citakoglu.  Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey , 2021, Arabian Journal of Geosciences.

[15]  H. Citakoglu,et al.  Reference Evapotranspiration Prediction from Limited Climatic Variables Using Support Vector Machines and Gaussian Processes , 2021, European Journal of Science and Technology.

[16]  Y. Serengil,et al.  Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey , 2021, Arabian Journal of Geosciences.

[17]  Mustafa Al-Mukhtar,et al.  Modeling of pan evaporation based on the development of machine learning methods , 2021, Theoretical and Applied Climatology.

[18]  Hyun IL Choi,et al.  Comment on Liu (2020): A rational performance criterion for hydrological model , 2021, Journal of Hydrology.

[19]  Shunsheng Wang,et al.  Energy budget for tomato plants grown in a greenhouse in northern China , 2021 .

[20]  H. Xi,et al.  Application of geodetector in sensitivity analysis of reference crop evapotranspiration spatial changes in Northwest China , 2021 .

[21]  Vahdettin DEMIR,et al.  Enhancing monthly lake levels forecasting using heuristic regression techniques with periodicity data component: application of Lake Michigan , 2021, Theoretical and Applied Climatology.

[22]  O. Kisi,et al.  Modeling reference evapotranspiration using a novel regression-based method: radial basis M5 model tree , 2021, Theoretical and Applied Climatology.

[23]  O. Kisi,et al.  Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering , 2021, Sustainability.

[24]  P. Gao,et al.  Influence of Meteorological Factors on the Potential Evapotranspiration in Yanhe River Basin, China , 2021, Water.

[25]  Jalal Shiri,et al.  Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches , 2021, Hydrology.

[26]  Sung Hyun Jung,et al.  Multi-Gene Genetic Programming Regression Model for Prediction of Transient Storage Model Parameters in Natural Rivers , 2020, Water.

[27]  Amir H. Alavi,et al.  Genetic programming in civil engineering: advent, applications and future trends , 2020, Artificial Intelligence Review.

[28]  S. Vicente‐Serrano,et al.  Climatology and trends of reference evapotranspiration in Spain , 2020, International Journal of Climatology.

[29]  Dedi Liu.  A rational performance criterion for hydrological model , 2020 .

[30]  B. Babayigit,et al.  Solar radiation prediction using multi-gene genetic programming approach , 2020, Theoretical and Applied Climatology.

[31]  Songjun Han,et al.  Quantitative Analysis of the Impact of Meteorological Factors on Reference Evapotranspiration Changes in Beijing, 1958–2017 , 2020, Water.

[32]  Yuk Feng Huang,et al.  Support vector machine enhanced empirical reference evapotranspiration estimation with limited meteorological parameters , 2020, Comput. Electron. Agric..

[33]  Majid Niazkar,et al.  COVID-19 Outbreak: Application of Multi-gene Genetic Programming to Country-based Prediction Models , 2020, Electronic Journal of General Medicine.

[34]  M. Zubair,et al.  Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and Machine Learning Algorithms in Four Climatic Regions , 2020, Pure and Applied Geophysics.

[35]  Mladen Todorovic,et al.  Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data , 2020 .

[36]  Majid Niazkar,et al.  Bridge backwater estimation: A Comparison between artificial intelligence models and explicit equations , 2020 .

[37]  A. Raza,et al.  Application of non-conventional soft computing approaches for estimation of reference evapotranspiration in various climatic regions , 2019, Theoretical and Applied Climatology.

[38]  Jazuli Abdullahi,et al.  Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements , 2019, Journal of Hydrology.

[39]  Devendra Prasad,et al.  System Model for Prediction Analytics Using K-Nearest Neighbors Algorithm , 2019, Journal of Computational and Theoretical Nanoscience.

[40]  M. Niazkar.  Revisiting the Estimation of Colebrook Friction Factor: A Comparison between Artificial Intelligence Models and C-W based Explicit Equations , 2019, KSCE Journal of Civil Engineering.

[41]  W. Zeng,et al.  Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions , 2019, Journal of Hydrology.

[42]  Shunlin Liang,et al.  Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[43]  Ming Wu,et al.  A Survey of Genetic Programming and Its Applications , 2019, KSII Trans. Internet Inf. Syst..

[44]  Jalal Shiri,et al.  Modeling reference evapotranspiration in island environments: Assessing the practical implications , 2019, Journal of Hydrology.

[45]  Ozgur Kisi,et al.  Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran) , 2019 .

[46]  N. Talebbeydokhti,et al.  Novel Grain and Form Roughness Estimator Scheme Incorporating Artificial Intelligence Models , 2018, Water Resources Management.

[47]  Keun-Chang Kwak,et al.  Prediction of short-term algal bloom using the M5P model-tree and extreme learning machine , 2018, Environmental Engineering Research.

[48]  Kavita Mittal,et al.  Performance study of K-nearest neighbor classifier and K-means clustering for predicting the diagnostic accuracy , 2018, International Journal of Information Technology.

[49]  M. Premalatha,et al.  Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study , 2018, Renewable and Sustainable Energy Reviews.

[50]  Peng Peng,et al.  Real-time road traffic state prediction based on kernel-KNN , 2018, Transportmetrica A: Transport Science.

[51]  Yongguo Yang,et al.  Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems , 2018, Comput. Electron. Agric..

[52]  Ozgur Kisi,et al.  Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree , 2018 .

[53]  Y. Ouyang,et al.  Evaluation of Using Remote Sensing Evapotranspiration Data in SWAT , 2018, Water Resources Management.

[54]  Ozgur Kisi,et al.  Evaluation of several soft computing methods in monthly evapotranspiration modelling , 2018 .

[55]  Sungwon Kim,et al.  Estimation of Long-Term Monthly Temperatures by Three Different Adaptive Neuro-Fuzzy Approaches Using Geographical Inputs , 2017 .

[56]  H. F. Blaney,et al.  Determining Water Requirements in Irrigated Areas From Climatological and Irrigation Data , 2017 .

[57]  Hatice Citakoglu,et al.  Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey , 2017, Theoretical and Applied Climatology.

[58]  Özgür Kisi,et al.  Using soil easily measured parameters for estimating soil water capacity: Soft computing approaches , 2017, Comput. Electron. Agric..

[59]  Mingming Huang,et al.  A novel approach for precipitation forecast via improved K-nearest neighbor algorithm , 2017, Adv. Eng. Informatics.

[60]  O. Kisi,et al.  Modifying Hargreaves–Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey , 2017 .

[61]  Mohammad Valipour,et al.  Analysis of potential evapotranspiration using limited weather data , 2017, Applied Water Science.

[62]  Ozgur Kisi,et al.  An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration , 2016, Theoretical and Applied Climatology.

[63]  Kelin Wang,et al.  Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN) , 2016, Theoretical and Applied Climatology.

[64]  Shahaboddin Shamshirband,et al.  Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine , 2016, Comput. Electron. Agric..

[65]  Ozgur Kisi,et al.  Evapotranspiration Estimation using Six Different Multi-layer PerceptronAlgorithms , 2016 .

[66]  J. Grieser,et al.  Calibration of the Hargreaves–Samani method for the calculation of reference evapotranspiration in different Köppen climate classes , 2016 .

[67]  Savaş Bayram,et al.  Comparison of multi layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: the case of Turkey , 2015 .

[68]  Marie Persson,et al.  Tree-Based Response Surface Analysis , 2015, MOD.

[69]  Özgür Kisi,et al.  Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data , 2015, Comput. Electron. Agric..

[70]  Ozgur Kisi,et al.  Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran , 2014 .

[71]  Ozgur Kisi,et al.  Evaluation of different data management scenarios for estimating daily reference evapotranspiration , 2013 .

[72]  P. N. Chatur,et al.  KNN Technique for Analysis and Prediction of Temperature and Humidity Data , 2013 .

[73]  Harianto Rahardjo,et al.  Unsaturated Soil Mechanics in Engineering Practice: Fredlund/Unsaturated Soil Mechanics , 2012 .

[74]  H. Kling,et al.  Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios , 2012 .

[75]  A. Gandomi,et al.  A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems , 2012, Neural Computing and Applications.

[76]  Joseph P. McFadden,et al.  Seasonal contributions of vegetation types to suburban evapotranspiration , 2011 .

[77]  Mac McKee,et al.  Forecasting daily potential evapotranspiration using machine learning and limited climatic data , 2011 .

[78]  Vijay P. Singh,et al.  Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree , 2010 .

[79]  Mahesh Pal,et al.  M5 model tree based modelling of reference evapotranspiration , 2009 .

[80]  Stephen R. Marsland,et al.  Machine Learning - An Algorithmic Perspective , 2009, Chapman and Hall / CRC machine learning and pattern recognition series.

[81]  Zailin Huo,et al.  Artificial neural network models for estimating regional reference evapotranspiration based on climate factors , 2009 .

[82]  Assefa M. Melesse,et al.  Global Daily Reference Evapotranspiration Modeling and Evaluation 1 , 2008 .

[83]  A. R. Khoob,et al.  Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment , 2008, Irrigation science.

[84]  Ozgur Kisi,et al.  Evapotranspiration modelling from climatic data using a neural computing technique , 2007 .

[85]  R. López-Urrea,et al.  An evaluation of two hourly reference evapotranspiration equations for semiarid conditions , 2006 .

[86]  A. D. Ghare,et al.  Discussion of ¿Simplified Estimation of Reference Evapotranspiration from Pan Evaporation Data in California¿ by Richard L. Snyder, Morteza Orang, Scott Matyac, and Mark E. Grismer , 2006 .

[87]  Slavisa Trajkovic,et al.  Temperature-based approaches for estimating reference evapotranspiration , 2005 .

[88]  Richard L. Snyder,et al.  Simplified Estimation of Reference Evapotranspiration from Pan Evaporation Data in California , 2005 .

[89]  Eibe Frank,et al.  Logistic Model Trees , 2003, Machine Learning.

[90]  Vijay P. Singh,et al.  Cross Comparison of Empirical Equations for Calculating Potential Evapotranspiration with Data from Switzerland , 2002 .

[91]  B. Gilmartin,et al.  The application of analysis of variance (ANOVA) to different experimental designs in optometry , 2002, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[92]  R. McCuen.  Hydrologic Analysis and Design , 1997 .

[93]  Michael Y. Hu,et al.  Effect of data standardization on neural network training , 1996 .

[94]  C. Rose,et al.  Pasture evapotranspiration under varying tree planting density in an agroforestry experiment , 1988 .

[95]  George H. Hargreaves,et al.  Reference Crop Evapotranspiration from Temperature , 1985 .

[96]  Johannes C. Guitjens,et al.  Models of Alfalfa Yield and Evapotranspiration , 1982 .

[97]  Bruce G. Batchelor,et al.  Pattern Recognition: Ideas in Practice , 1978 .

[98]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[99]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[100]  H. L. Penman.  Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[101]  M. Lurie,et al.  Evaporation from Free Water Surface , 1936 .

[102]  Yufang Jin,et al.  Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling , 2022, Remote. Sens..

[103]  M. Özger,et al.  Estimation of measured evapotranspiration using data-driven methods with limited meteorological variables , 2021, Italian Journal of Agrometeorology.

[104]  Predictive Modelling for Energy Management and Power Systems Engineering , 2021 .

[105]  Ravinesh C. Deo,et al.  Development of data-driven models for wind speed forecasting in Australia , 2021 .

[106]  Sushma Jain,et al.  Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning , 2019, Comput. Electron. Agric..

[107]  Zaher Mundher Yaseen,et al.  Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios , 2018, Theoretical and Applied Climatology.

[108]  A. Chhabra.  Road Traffic Prediction Using KNN and Optimized Multilayer Perceptron , 2018 .

[109]  Ozgur Kisi,et al.  Hydrological Time Series Forecasting Using Three Different Heuristic Regression Techniques , 2017 .

[110]  Doreen Eichel,et al.  Unsaturated Soil Mechanics In Engineering Practice , 2016 .

[111]  Michael D. Dukes,et al.  Step by Step Calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method) , 2024, EDIS.

[112]  Yogesh Singh,et al.  Suitability of KNN Regression in the Development of Interaction based Software Fault Prediction Models , 2014 .

[113]  Ozgur Kisi,et al.  Estimation of Monthly Mean Reference Evapotranspiration in Turkey , 2013, Water Resources Management.

[114]  S. Imandoust,et al.  Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background , 2013 .

[115]  Luis S. Pereira,et al.  Crop Water Requirements , 2013 .

[116]  Gartenbau Forstwirtschaft Fischerei Ernährung Landwirtschaft.  Institute of Food and Agricultural Sciences , 2011 .

[117]  Dominic P. Searson,et al.  GPTIPS: An Open Source Genetic Programming Toolbox For Multigene Symbolic Regression , 2010 .

[118]  Dominic P. Searson,et al.  GPTIPS: Genetic Programming and Symbolic Regression for Matlab , 2009 .

[119]  M. Butts,et al.  Effectiveness of complex physics and DTM based distributed models for flood risk management of the River Tone, UK , 2009 .

[120]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[121]  J. R. Quinlan.  Learning With Continuous Classes , 1992 .

[122]  Edwin Diday,et al.  A Recent Advance in Data Analysis: Clustering Objects into Classes Characterized by Conjunctive Concepts , 1981 .

[123]  G. EARL HARBECK,et al.  A practical field technique for measuring reservoir evaporation utilizing mass-transfer theory , 1962 .