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

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...

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Autores principales:	Serdar Neslihanoglu, Ecem Ünal, Ceylan Yozgatlıgil
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ets kalman filter nnetar precipitation prophet tbats Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/340b2d5f9ffa4da9bd6fa6d695a1f46c
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spelling	oai:doaj.org-article:340b2d5f9ffa4da9bd6fa6d695a1f46c2021-11-05T18:52:10ZPerformance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey2040-22442408-935410.2166/wcc.2021.332https://doaj.org/article/340b2d5f9ffa4da9bd6fa6d695a1f46c2021-06-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/4/1071https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354Condensed 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 Muğla 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 Muğla region, encouraging the time-varying coefficients extensions of the precipitation model. HIGHLIGHTS The modelling and forecasting of precipitation amount are difficult because of its highly parametrized and varied nature.; The performances of filtering methods, namely the multiple linear regression, the state space model (SSM), hybrid, SARIMA, ETS, TBATS, NNETAR and Prophet models on monthly total precipitation amount are investigated.; The results support SSM when modelling and forecasting the total precipitation amount.;Serdar NeslihanogluEcem ÜnalCeylan YozgatlıgilIWA Publishingarticleetskalman filternnetarprecipitationprophettbatsEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 4, Pp 1071-1085 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ets kalman filter nnetar precipitation prophet tbats Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	ets kalman filter nnetar precipitation prophet tbats Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Serdar Neslihanoglu Ecem Ünal Ceylan Yozgatlıgil Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey
description	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 Muğla 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 Muğla region, encouraging the time-varying coefficients extensions of the precipitation model. HIGHLIGHTS The modelling and forecasting of precipitation amount are difficult because of its highly parametrized and varied nature.; The performances of filtering methods, namely the multiple linear regression, the state space model (SSM), hybrid, SARIMA, ETS, TBATS, NNETAR and Prophet models on monthly total precipitation amount are investigated.; The results support SSM when modelling and forecasting the total precipitation amount.;
format	article
author	Serdar Neslihanoglu Ecem Ünal Ceylan Yozgatlıgil
author_facet	Serdar Neslihanoglu Ecem Ünal Ceylan Yozgatlıgil
author_sort	Serdar Neslihanoglu
title	Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey
title_short	Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey
title_full	Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey
title_fullStr	Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey
title_full_unstemmed	Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey
title_sort	performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for muğla in turkey
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/340b2d5f9ffa4da9bd6fa6d695a1f46c
work_keys_str_mv	AT serdarneslihanoglu performancecomparisonoffilteringmethodsonmodellingandforecastingthetotalprecipitationamountacasestudyformuglainturkey AT ecemunal performancecomparisonoffilteringmethodsonmodellingandforecastingthetotalprecipitationamountacasestudyformuglainturkey AT ceylanyozgatlıgil performancecomparisonoffilteringmethodsonmodellingandforecastingthetotalprecipitationamountacasestudyformuglainturkey
_version_	1718444093703454720

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

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