Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China

[1]  Ozgur Kisi,et al.  Modeling reference evapotranspiration using three different heuristic regression approaches , 2016 .

[2]  Mario Córdova,et al.  Evaluation of the Penman-Monteith (FAO 56 PM) Method for Calculating Reference Evapotranspiration Using Limited Data , 2015 .

[3]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[4]  Omid Bozorg-Haddad,et al.  Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm , 2019, Journal of Hydrology.

[5]  Junliang Fan,et al.  Effects of earlywood and latewood on sap flux density-based transpiration estimates in conifers , 2018 .

[6]  N. Mahowald,et al.  Global review and synthesis of trends in observed terrestrial near-surface wind speeds; implications for evaporation , 2012 .

[7]  Shahaboddin Shamshirband,et al.  Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission , 2014 .

[8]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[9]  Qiliang Yang,et al.  Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models , 2020 .

[10]  Dalibor Petković,et al.  Prediction of laser welding quality by computational intelligence approaches , 2017 .

[11]  Lifeng Wu,et al.  Potential of kernel-based nonlinear extension of Arps decline model and gradient boosting with categorical features support for predicting daily global solar radiation in humid regions , 2019, Energy Conversion and Management.

[12]  Junliang Fan,et al.  Machine learning models for the estimation of monthly mean daily reference evapotranspiration based on cross-station and synthetic data , 2019, Hydrology Research.

[13]  Shahaboddin Shamshirband,et al.  Estimation of Reference Evapotranspiration Using Neural Networks and Cuckoo Search Algorithm , 2016 .

[14]  J. Valiantzas.  Simplified limited data Penman’s ET0 formulas adapted for humid locations , 2015 .

[15]  Lifeng Wu,et al.  Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration , 2019, PloS one.

[16]  Junliang Fan,et al.  Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models , 2020 .

[17]  Chi-Hua Chen,et al.  Deep Learning Techniques for Agronomy Applications , 2019, Agronomy.

[18]  Dalibor Petković,et al.  Wind farm efficiency by adaptive neuro-fuzzy strategy , 2016 .

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

[20]  Shahaboddin Shamshirband,et al.  Forecasting of Underactuated Robotic Finger Contact Forces by Support Vector Regression Methodology , 2016, Int. J. Pattern Recognit. Artif. Intell..

[21]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[22]  Dalibor Petković,et al.  Analyzing of flexible gripper by computational intelligence approach , 2016 .

[23]  G. Baiamonte,et al.  Analytical approach extending the Granier method to radial sap flow patterns , 2020 .

[24]  Zhibin He,et al.  Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions , 2015, Water Resources Management.

[25]  Yi Li,et al.  Drought evolution, severity and trends in mainland China over 1961-2013. , 2018, The Science of the total environment.

[26]  Lifeng Wu,et al.  Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: Application in contrasting climates of China , 2019, Journal of Hydrology.

[27]  Ling Tong,et al.  Evaluating eddy covariance method by large-scale weighing lysimeter in a maize field of northwest China , 2010 .

[28]  Anil Kumar Singh,et al.  Estimation of crop coefficient of irrigated transplanted puddled rice by field scale water balance in the semi-arid Indo-Gangetic Plains, India , 2016 .

[29]  Yong Peng,et al.  Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data , 2017, Comput. Electron. Agric..

[30]  Dalibor Petković,et al.  Selection of the most influential factors on the water-jet assisted underwater laser process by adaptive neuro-fuzzy technique , 2016 .

[31]  Dalibor Petković,et al.  Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms , 2017 .

[32]  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.

[33]  J. Porter,et al.  Choice of the Ångström–Prescott coefficients: Are time-dependent ones better than fixed ones in modeling global solar irradiance? , 2010 .

[34]  Koffi Djaman,et al.  Evaluation of the Penman–Monteith reference evapotranspiration under limited data and its sensitivity to key climatic variables under humid and semiarid conditions , 2018, Modeling Earth Systems and Environment.

[35]  Dalibor Petković,et al.  Precipitation concentration index management by adaptive neuro-fuzzy methodology , 2017, Climatic Change.

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

[37]  Dalibor Petkovic,et al.  Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique , 2017, Knowledge and Information Systems.

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

[39]  Lifeng Wu,et al.  Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models , 2018, Journal of Hydrology.

[40]  Lifeng Wu,et al.  Estimation of daily dew point temperature by using bat algorithm optimization based extreme learning machine , 2020 .

[41]  Shahaboddin Shamshirband,et al.  Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration , 2016, Theoretical and Applied Climatology.

[42]  Siti Hafizah Ab Hamid,et al.  Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect , 2014, Natural Hazards.

[43]  Kasra Mohammadi,et al.  A support vector machine–firefly algorithm-based model for global solar radiation prediction , 2015 .

[44]  Shahaboddin Shamshirband,et al.  Evaluation of modulation transfer function of optical lens system by support vector regression methodologies A comparative study , 2014 .

[45]  Xin Ma,et al.  Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction , 2020, Comput. Electron. Agric..

[46]  Songbing Zou,et al.  Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area , 2017 .

[47]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[48]  Dalibor Petkovic,et al.  Statistical evaluation of mathematics lecture performances by soft computing approach , 2018, Comput. Appl. Eng. Educ..

[49]  M. Singh,et al.  A novel approach for rainfall-runoff modelling using a biogeography-based optimization technique , 2019, International Journal of River Basin Management.

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

[51]  Mohammadamin Azimi,et al.  Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer , 2020 .

[52]  M. Exner-Kittridge,et al.  Case Study on the Accuracy and Cost/Effectiveness in Simulating Reference Evapotranspiration in West-Central Florida , 2010 .

[53]  Z. Yaseen,et al.  Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso , 2018, Agricultural Water Management.

[54]  Dalibor Petković,et al.  Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation , 2013 .

[55]  Yuanyuan Zha,et al.  A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China , 2020 .

[56]  Q. Zhuang,et al.  Evapotranspiration partitioning and water productivity of rainfed maize under contrasting mulching conditions in Northwest China , 2021 .

[57]  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 .

[58]  Srđan Jović,et al.  Vibration analyzing in horizontal pumping aggregate by soft computing , 2018, Measurement.

[59]  Ozgur Kisi,et al.  Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing , 2015 .

[60]  Ainuddin Wahid Abdul Wahab,et al.  Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission , 2014 .

[61]  Shervin Motamedi,et al.  Soft computing approaches for forecasting reference evapotranspiration , 2015, Comput. Electron. Agric..

[62]  H. Cai,et al.  Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China , 2018, Agricultural and Forest Meteorology.

[63]  Jalal Shiri,et al.  Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran , 2017 .

[64]  Shahaboddin Shamshirband,et al.  Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study , 2015, IEEE Sensors Journal.

[65]  Min Yan Chia,et al.  Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine , 2021 .

[66]  Jia Yue,et al.  Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China , 2017 .

[67]  Xin Ma,et al.  Predicting the oil production using the novel multivariate nonlinear model based on Arps decline model and kernel method , 2016, Neural Computing and Applications.

[68]  Lifeng Wu,et al.  Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China , 2019, Renewable and Sustainable Energy Reviews.

[69]  Koffi Djaman,et al.  Evaluation of the Penman-Monteith and other 34 reference evapotranspiration equations under limited data in a semiarid dry climate , 2018, Theoretical and Applied Climatology.

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