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akaturk Academic measurement

Academician profile · DOÇENT

AMIN GHAREHBAGHI

HASAN KALYONCU ÜNİVERSİTESİ

  • Ana Dal Mühendislik Temel Alanı
  • Yan Dal İnşaat Mühendisliği
  • MÜHENDİSLİK FAKÜLTESİ
  • İNŞAAT MÜHENDİSLİĞİ BÖLÜMÜ
Articles 64
Projects 0
Books 1
Proceedings 5
Patents 1
Artistic 1
Scopus (SJR)
Q1
31
Q2
21
Q3
5
Q4
0
WoS (JCR)
Q1
29
Q2
11
Q3
13
Q4
4
TR Index
4 articles

Index and quartile filters

64 publications total

Scopus (SJR)
Q1 31 Q2 21 Q3 5 Q4 0
WoS (JCR)
Q1 29 Q2 11 Q3 13 Q4 4
TR Index
4 articles

Articles

  1. 2026 Advancement in forecasting rainfall-runoff process: application of a novel hybrid FMD-SVR-ABC modeling technique Acta Geophysica DOI 10.1007/s11600-026-01878-5
  2. 2026 Next-generation intelligent framework for pan evaporation prediction: introducing Chebyshev polynomial-based Kolmogorov-Arnold networks Scientific Reports DOI 10.1038/s41598-026-53769-w
  3. 2026 Monthly and fixed coefficient calibration of empirical reference evapotranspiration equations: A case study for a Mediterranean climate (Izmir Province) Journal of Hydrology and Hydromechanics DOI 10.2478/johh-2026-0014
  4. 2026 Application of Hybrid Meta-Heuristic-Based Data‑Driven Models to Forecast Streamflow Drought Index Water Resources Management DOI 10.1007/s11269-026-04682-4
  5. 2026 Advancing river streamflow prediction with convolutional Kolmogorov-Arnold network: a comprehensive comparison against machine learning and deep learning models Engineering Applications of Computational Fluid Mechanics DOI 10.1080/19942060.2026.2643817
  6. 2026 Application of Novel Hybrid SVMD-NARX-ACO Model for Precise Predicting Monthly Pan Evaporation ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING DOI 10.1007/s13369-026-11115-2
  7. 2026 A Comparative Evaluation of Deep Learning and Machine Learning Models for River Suspended Sediment Concentration Forecasting WATER RESOURCES MANAGEMENT DOI 10.1007/s11269-025-04401-5
  8. 2026 An advanced complex hybrid machine learning model for forecasting reference evapotranspiration PHYSICS AND CHEMISTRY OF THE EARTH DOI 10.1016/j.pce.2026.104382
  9. 2025 Correction: Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river’s streamflow Scientific Reports DOI 10.1038/s41598-025-10510-3
  10. 2025 Development of interpretable intelligent frameworks for estimating river water turbidity Engineering Applications of Computational Fluid Mechanics DOI 10.1080/19942060.2025.2511886
  11. 2025 A Comparative Study on Novel Hybrid Approaches Based on CEEMDAN, Random Forest, Deep Learning Methods for Predicting Daily Wind Speed Earth Systems and Environment DOI 10.1007/s41748-025-00714-y
  12. 2025 Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river’s streamflow Scientific Reports DOI 10.1038/s41598-025-03185-3
  13. 2025 Developing a novel layer network structure for a LSTM model to predict mean monthly river streamflow Applied Water Science DOI 10.1007/s13201-025-02535-w
  14. 2025 A Comparative Study on Novel Hybrid Approaches Based on CEEMDAN, Random Forest, Deep Learning Methods for Predicting Daily Wind Speed EARTH SYSTEMS AND ENVIRONMENT DOI 10.1007/s41748-025-00714-y
  15. 2025 Development of interpretable intelligent frameworks for estimating river water turbidity ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS DOI 10.1080/19942060.2025.2511886
  16. 2025 Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river's streamflow SCIENTIFIC REPORTS DOI 10.1038/s41598-025-03185-3
  17. 2025 A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting WATER RESOURCES MANAGEMENT DOI 10.1007/s11269-024-04052-y
  18. 2025 Development of deep learning approaches for drought forecasting: a comparative study in a cold and semi-arid region EARTH SCIENCE INFORMATICS DOI 10.1007/s12145-024-01650-7
  19. 2025 Developing a novel layer network structure for a LSTM model to predict mean monthly river streamflow APPLIED WATER SCIENCE DOI 10.1007/s13201-025-02535-w
  20. 2025 Development of explainable hybrid quantum-inspired recurrent neural networks for predicting groundwater quality: A case study at West Azerbaijan, Iran JOURNAL OF WATER PROCESS ENGINEERING DOI 10.1016/j.jwpe.2025.109013

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