Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran
暂无分享,去创建一个
[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 .