Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

[1]  Z. Yaseen,et al.  Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection , 2022, Agricultural Water Management.

[2]  Dinesh Kr. Vishwakarma,et al.  Watershed prioritization using morphometric analysis by MCDM approaches , 2022, Ecol. Informatics.

[3]  Z. Yaseen,et al.  Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer , 2022, Comput. Electron. Agric..

[4]  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.

[5]  Sujay Raghavendra Naganna,et al.  Comparative evaluation of deep learning and machine learning in modelling pan evaporation using limited inputs , 2022, Hydrological Sciences Journal.

[6]  Prabhakar Sharma,et al.  An ANFIS-RSM based modeling and multi-objective optimization of syngas powered dual-fuel engine , 2022, International Journal of Hydrogen Energy.

[7]  Dinesh Kr. Vishwakarma,et al.  Methods to estimate evapotranspiration in humid and subtropical climate conditions , 2022, Agricultural Water Management.

[8]  Ö. Kisi,et al.  Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms , 2021, Comput. Electron. Agric..

[9]  S. Sharafi,et al.  Calibration of empirical equations for estimating reference evapotranspiration in different climates of Iran , 2021, Theoretical and Applied Climatology.

[10]  Junliang Fan,et al.  A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China , 2021 .

[11]  S. Sharafi,et al.  Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran , 2021, Theoretical and Applied Climatology.

[12]  Shijun Sun,et al.  Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods , 2020 .

[13]  T. Russo,et al.  A Snapshot of the World's Groundwater Challenges , 2020 .

[14]  Saad Shauket Sammen,et al.  Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm , 2020, Environmental Monitoring and Assessment.

[15]  F. Qi,et al.  Evaluation of 32 Simple Equations against the Penman–Monteith Method to Estimate the Reference Evapotranspiration in the Hexi Corridor, Northwest China , 2020, Water.

[16]  Adil Salhi,et al.  Comparative assessment of different reference evapotranspiration models towards a fit calibration for arid and semi-arid areas , 2020, Journal of Arid Environments.

[17]  Daniel Althoff,et al.  Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier , 2020, Theoretical and Applied Climatology.

[18]  S. Sharafi,et al.  Investigating trend changes of annual mean temperature and precipitation in Iran , 2020, Arabian Journal of Geosciences.

[19]  S. Alexandris,et al.  How significant is the effect of the surface characteristics on the Reference Evapotranspiration estimates? , 2020 .

[20]  S. Mehdizadeh,et al.  Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm , 2020, Agricultural Water Management.

[21]  Yu Feng,et al.  Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data , 2020, Comput. Electron. Agric..

[22]  O. Kisi,et al.  Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration , 2020, Environmental Science and Pollution Research.

[23]  G. Destouni,et al.  Variability and change in the hydro-climate and water resources of Iran over a recent 30-year period , 2020, Scientific Reports.

[24]  F. Üneş,et al.  Daily reference evapotranspiration prediction based on climatic conditions applying different data mining techniques and empirical equations , 2020, Theoretical and Applied Climatology.

[25]  G. Vourlitis,et al.  Comparative assessment of modelled and empirical reference evapotranspiration methods for a brazilian savanna , 2020 .

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

[27]  Yi Li,et al.  The spatiotemporal variations of soil water content and soil temperature and the influences of precipitation and air temperature at the daily, monthly, and annual timescales in China , 2020, Theoretical and Applied Climatology.

[28]  Santos Henrique Brant Dias,et al.  Impact of sum-of-hourly and daily timesteps in the computations of reference evapotranspiration across the Brazilian territory , 2019 .

[29]  M. Scholz,et al.  Climate Variability Impact on the Spatiotemporal Characteristics of Drought and Aridityin Arid and Semi-Arid Regions , 2019, Water Resources Management.

[30]  Anurag Malik,et al.  Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches , 2019, Hydrological Sciences Journal.

[31]  Anurag Malik,et al.  The viability of co-active fuzzy inference system model for monthly reference evapotranspiration estimation: case study of Uttarakhand State , 2019, Hydrology Research.

[32]  M. Bierkens,et al.  Environmental flow limits to global groundwater pumping , 2019, Nature.

[33]  Matheus Mendes Reis,et al.  Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data , 2019, Comput. Electron. Agric..

[34]  Hongsong Chen,et al.  Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China , 2019, Agricultural Water Management.

[35]  Lucas Borges Ferreira,et al.  Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach , 2019, Journal of Hydrology.

[36]  Ivan Glesk,et al.  Tuning machine learning models for prediction of building energy loads , 2019, Sustainable Cities and Society.

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

[38]  Jalal Shiri,et al.  Global comparison of 20 reference evapotranspiration equations in a semi-arid region of Iran , 2019 .

[39]  A. Seifi,et al.  Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models , 2018, Environmental Science and Pollution Research.

[40]  Ningbo Cui,et al.  Improvement of Makkink model for reference evapotranspiration estimation using temperature data in Northwest China , 2018, Journal of Hydrology.

[41]  Yue Jia,et al.  National-scale assessment of pan evaporation models across different climatic zones of China , 2018, Journal of Hydrology.

[42]  Babak Mohammadi,et al.  Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran , 2018, Theoretical and Applied Climatology.

[43]  Srdjan Jovic,et al.  Evolutionary algorithm for reference evapotranspiration analysis , 2018, Comput. Electron. Agric..

[44]  Mohamed A. Mattar,et al.  Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt , 2018 .

[45]  Shahaboddin Shamshirband,et al.  Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran , 2018 .

[46]  Ningbo Cui,et al.  Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. , 2017 .

[47]  Ozgur Kisi,et al.  Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations , 2017, Theoretical and Applied Climatology.

[48]  Mehdi Khiadani,et al.  Calibration of Valiantzas’ reference evapotranspiration equations for the Pilbara region, Western Australia , 2017, Theoretical and Applied Climatology.

[49]  G. Kamali,et al.  Role of early warning systems for sustainable agriculture in Iran , 2016, Arabian Journal of Geosciences.

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

[51]  R. Deo,et al.  Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models , 2016, Stochastic Environmental Research and Risk Assessment.

[52]  M. Khiadani,et al.  Developing Equations for Estimating Reference Evapotranspiration in Australia , 2016, Water Resources Management.

[53]  Yu Feng,et al.  Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China , 2016 .

[54]  Paresh Chandra Deka,et al.  An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs , 2016, Comput. Electron. Agric..

[55]  Shahaboddin Shamshirband,et al.  Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation , 2016, Environmental Earth Sciences.

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

[57]  Jan Adamowski,et al.  Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS , 2014, Expert Syst. Appl..

[58]  H. Tabari,et al.  Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration , 2013, Irrigation Science.

[59]  Hongbin Liu,et al.  General models for estimating daily global solar radiation for different solar radiation zones in mainland China , 2013 .

[60]  Jamshid Piri,et al.  Daily Pan Evaporation Modelling With ANFIS and NNARX , 2013 .

[61]  Ozgur Kisi,et al.  Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) , 2013 .

[62]  O. Kisi,et al.  SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment , 2012 .

[63]  Dachun Chen,et al.  Daily Reference Evapotranspiration Estimation Based on Least Squares Support Vector Machines , 2011, CCTA.

[64]  Hung Soo Kim,et al.  Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling , 2008 .

[65]  L. S. Pereira,et al.  A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman-Monteith method , 2006 .

[66]  Gavin C. Cawley,et al.  Fast exact leave-one-out cross-validation of sparse least-squares support vector machines , 2004, Neural Networks.

[67]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[68]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[69]  C.C.Y. Ma,et al.  Statistical comparison of solar radiation correlations Monthly average global and diffuse radiation on horizontal surfaces , 1983 .

[70]  W. Baier,et al.  ESTIMATION OF LATENT EVAPORATION FROM SIMPLE WEATHER OBSERVATIONS , 1965 .

[71]  H. R. Haise,et al.  Estimating evapotranspiration from solar radiation , 1963 .

[72]  M. A. Kohler,et al.  On the Use of Double-Mass Analysis for Testing the Consistency of Meteorological Records and for Making Required Adjustments , 1949 .

[73]  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.

[74]  Ashima Singh,et al.  DeepEvap: Deep reinforcement learning based ensemble approach for estimating reference evapotranspiration , 2022, Appl. Soft Comput..

[75]  A. Reif,et al.  Evaluation of Radiation-Based Reference Evapotranspiration Models Under Different Mediterranean Climates in Central Greece , 2013, Water Resources Management.

[76]  G. Tsakiris,et al.  The effect of PET method on Reconnaissance Drought Index (RDI) calculation , 2013 .

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