Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Mugla in Turkey

NESLİHANOĞLU S., ÜNAL E., YOZGATLIGİL C.

JOURNAL OF WATER AND CLIMATE CHANGE, cilt.12, sa.4, ss.1071-1085, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.2166/wcc.2021.332
  • Dergi Adı: JOURNAL OF WATER AND CLIMATE CHANGE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Compendex, Geobase, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1071-1085
  • Anahtar Kelimeler: ETS, hybrid model, Kalman filter, NNETAR, precipitation, prophet, TBATS, NEURAL-NETWORKS, MAXIMUM PRECIPITATION, TIME-SERIES, PREDICTION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Condensed water vapor in the atmosphere is observed as precipitation whenever moist air rises sufficiently enough to produce saturation, condensation, and the growth of precipitation particles. It is hard to measure the amount and concentration of total precipitation over time due to the changes in the amount of precipitation and the variability of climate. As a result of these, the modelling and forecasting of precipitation amount is challenging. For this reason, this study compares forecasting performances of different methods on monthly precipitation series with covariates including the temperature, relative humidity, and cloudiness of Mula region, Turkey. To accomplish this, the performance of multiple linear regression, the state space model (SSM) via Kalman Filter, a hybrid model integrating the logistic regression and SSM models, the seasonal autoregressive integrated moving average (SARIMA), exponential smoothing with state space model (ETS), exponential smoothing state space model with Box-Cox transformation-ARMA errors-trend and seasonal components (TBATS), feed-forward neural network (NNETAR) and Prophet models are all compared. This comparison has yet to be undertaken in the literature. The empirical findings overwhelmingly support the SSM when modelling and forecasting the monthly total precipitation amount of the Mula region, encouraging the time-varying coefficients extensions of the precipitation model.