Evaporation modelling using soft computing techniques

DALKILIÇ H. Y.

Fresenius Environmental Bulletin, cilt.29, sa.8, ss.6461-6468, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 8
  • Basım Tarihi: 2020
  • Dergi Adı: Fresenius Environmental Bulletin
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Chemical Abstracts Core, Communication Abstracts, Environment Index, Geobase, Greenfile, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.6461-6468
  • Anahtar Kelimeler: Evaporation, Extreme learning machine, Gaussian process regression, Minimax probability machine regression, Prediction, Reservoir
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

© by PSPEvaporation, which is one of the most important components of the hydrological cycle, is of great importance for developing, planning, operating, and managing water resources. In the present study, the average weekly evaporation and other hydrometeorological data measured by Manasgoan [1] between 1990 and 2004 were modelled using extreme learning machine (ELM), minimax probability machine regression (MPMR), and Gaussian process regression (GPR) methods. Wind speed, air temperature, relative humidity, and the number of sunshine hours were used as model input, and evaporation was the output. The correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and performance index were used as performance criteria in the evaluation of the model results. The model results indicated that the Gaussian process regression (GPR) model is more accurate and provides more successful results compared to other methods.