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Disambiguation of "Ch, S."

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Soft computing approaches for forecasting reference evapotranspiration

, , , , , , and . Computers and Electronics in Agriculture, (2015)cited By 22.
DOI: 10.1016/j.compag.2015.02.010

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Yu-s ng Ch ng

n-s k Ch ng

Kyo-Ch Ch ng

Ch Gutenbrunner

Ch Parasher

 

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Surface roughness prediction by extreme learning machine constructed with abrasive water jet, , , , , , , and . Precision Engineering, (2016)cited By 10.Soft computing approaches for forecasting reference evapotranspiration, , , , , , and . Computers and Electronics in Agriculture, (2015)cited By 22.Long-Term Precipitation Analysis and Estimation of Precipitation Concentration Index Using Three Support Vector Machine Methods, , , , , and . Advances in Meteorology, (2016)cited By 2.Application of extreme learning machine for estimation of wind speed distribution, , , , , , , and . Climate Dynamics, 46 (5-6): 1893-1907 (2016)cited By 19.Predicting the wind power density based upon extreme learning machine, , , , , and . Energy, (2015)cited By 27.Extreme learning machine approach for sensorless wind speed estimation, , , , , and . Mechatronics, (2016)cited By 7.Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm, , , , , , and . Energy, (2016)cited By 12.A combined method to estimate wind speed distribution based on integrating the support vector machine with firefly algorithm, , , , , and . Environmental Progress and Sustainable Energy, 35 (3): 867-875 (2016)cited By 5.A hybrid SVM-FFA method for prediction of monthly mean global solar radiation, , , , , and . Theoretical and Applied Climatology, 125 (1-2): 53-65 (2016)cited By 3.Erratum to: Application of extreme learning machine for estimation of wind speed distribution (Climate Dynamics, DOI: 10.1007/s00382-015-2682-2), , , , , , , and . Climate Dynamics, 46 (5-6): 2025 (2016)cited By 4.