Improving precipitation estimates for Turkey with multimodel ensemble: a comparison of nonlinear artificial neural network method with linear methods


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Mesta B., AKGÜN Ö. B., KENTEL ERDOĞAN E.

Neural Computing and Applications, cilt.36, sa.17, ss.10219-10238, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 17
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s00521-024-09598-x
  • Dergi Adı: Neural Computing and Applications
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.10219-10238
  • Anahtar Kelimeler: Artificial neural network, Multiple linear regression, Precipitation, RCM
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Ensemble analysis is proven to provide advantages in climate change impact assessment based on outputs from climate models. Ensembled series are shown to outperform single-model assessments through increased consistency and stability. This study aims to test the improvement of precipitation estimates through the use of ensemble analysis for south and southwestern Turkey which is known to have complex climatic features due to varying topography and interacting climate forcings. The analysis covers an evaluation of the performance of eight regional climate models (RCMs) from the EUR-11 domain available from the CORDEX database. The historical outputs are evaluated for their representativeness of the current climate of the Mediterranean region and its surroundings in Turkey through a comparison with long-term monthly precipitation time series obtained from ground-based precipitation observations by the use of statistical performance indicators and Taylor diagrams. This is followed by a comparative evaluation of three ensemble methodologies, simple average of the models, multiple linear regression for superensemble, and artificial neural networks (ANN). The analysis results show that the overall performance of ensembled time series is better compared to individual RCMs. ANN generally provided the best performance when all RCMs are used as inputs. Improvement in the performance of ensembling due to the use of nonlinear models is further confirmed by fuzzy inference systems (FIS). Both ANN and FIS generated monthly precipitation time series with higher correlations with those of observations. However, extreme events are poorly represented in the ensembled time series, and this may result in inefficiency in the design of various water structures such as spillways and storm water drainage systems that are based on high return period events.