Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Journal of Water and Land Development

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: stationarity test

Sortuj według:

Ogranicz wyniki do:

The SARIMA model-based monthly rainfall forecasting for the Turksvygbult Station at the Magoebaskloof Dam in South Africa

Tadesse Kassahun Birhanu, Dinka Megersa Olumana

Journal of Water and Land Development

2022

no. 53

100--107

Rainfall forecast information is important for the planning and management of water resources and agricultural activities. Turksvygbult rainfall near the Magoebaskloof Dam (South Africa) has never been modelled and forecasted. Hence, the objective of this study was to forecast its monthly rainfall using the SARIMA model. GReTL and automatic XLSTAT software were used for forecasting. The trend of the long-term rainfall time series (TS) was tested by Mann-Kendall and its stationarity was proved by various unit root tests. The TS data from Oct 1976 to Sept 2015 were used for model training and the remaining data (Oct 2015 to Sept 2018) for validation. Then, all TS (Oct 1976 to Sept 2018) were used for out of sample forecasting. Several SARIMA models were identified using correlograms that were derived from seasonally differentiated TS. Model parameters were derived by the maximum likelihood method. Residual correlogram and Ljung–Box Q tests were used to check the forecast accuracy. Based on minimum Akaike information criteria (AI) value of 5642.69, SARIMA (2, 0, 3) (3, 1, 3)12 model was developed using GReTL as the best of all models. SARIMA (1, 0, 1) (3, 1, 3)12, with minimum AI value of 5647.79, was the second-best model among GReTl models. This second model was also the first best automatically selected model by XLSTAT. In conclusion, these two best models can be used by managers for rainfall forecasting and management of water resources and agriculture, and thereby it can contribute to economic growth in the study area. Hence, the developed SARIMA forecasting procedure can be used for forecasting of rainfall and other time series in different areas.

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Tadesse K. B., Dinka M. O.

Journal of Water and Land Development

2017

no. 35

229--236

Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan–Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

Znajomość przyszłego przepływu wody w rzece jest istotna dla rozwoju i zarządzania w systemie rzecznym. W badaniach prezentowanych w niniejszym artykule prognozowano przepływ w rzece Waterval w Republice Południowej Afryki, używając modelu SARIMA i programu statystycznego GRETL. Do modelowania i budowania prognoz wykorzystano średnie miesięczne przepływy z lat 1960–2016. Różne pierwiastki jednostkowe i analiza trendu Manna–Kendalla dowiodły stacjonarności obserwowanych szeregów czasowych przepływu. Na podstawie sezonowo zróżnicowanych charakterystyk korelogramu oceniono różne modele SARIMA zoptymalizowano ich parametry i wykonano diagnostyczne sprawdzenie prognoz za pomocą białego szumu i testów heteroscedastyczności. Na podstawie minimum AI i kryteriów Hannana–Quinna (HQ), wybrano model SARIMA (3, 0, 2) x (3, 1, 3)12 do prognozowania przepływu w rzece Waterval. W dalszych badaniach proponuje się porównanie prognozowania za pomocą modeli SARIMA i technik komputerowych.