Estimation of monthly evaporation values using gradient boosting machines and mode decomposition techniques in the Southeast Anatolia Project (GAP) area in Turkey


Sarıgöl M., Katipoğlu O. M.

Acta Geophysica, cilt.72, sa.2, ss.999-1016, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 72 Sayı: 2
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s11600-023-01067-8
  • Dergi Adı: Acta Geophysica
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.999-1016
  • Anahtar Kelimeler: Evaporation, Empirical mode decomposition, Machine learning, Variational mode decomposition, Gradient boosting machines, Southeast Anatolia Project (GAP) area
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

Today, the biggest issue appears to be the increase in drought in some regions brought on by global warming, which has greatly increased the significance of water management. In light of evaporation's effect on drought, this research intends to evaluate the effectiveness of hybrid machine learning (ML) models, such as the Gradient Boosting Machines (GBM) technique paired with Empirical Mode Decomposition (EMD), Robust Empirical Mode Decomposition (REMD), Ensemble Empirical Mode Decomposition (EEMD), and Variational Mode Decomposition (VMD) signal decomposition techniques, for monthly evaporation prediction models in the Southeast Anatolia Project Area. In the design of the models, 80% of the data was used for training and 20% for testing. Furthermore, tenfold cross-validation was applied to solve the overfitting problem, which negatively affected the forecast performance. In the model setup, various combinations of precipitation, average air temperature, minimum air temperature, maximum air temperature, wind speed, actual air pressure, relative humidity, and solar time variables are presented to artificial intelligence models as input. The study revealed that the GBM methodology in combination with the signal decomposition methods REMD, EMD, EEMD, and VMD generally allowed for more accurate evaporation estimations than the GBM model alone. The study’s results are essential in relation to agricultural production, irrigation planning, water resources management studies, and hydrological modeling studies in the region.