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

• Autorzy: Tadesse Kassahun Birhanu , Dinka Megersa Olumana

Identyfikatory

DOI

10.24425/jwld.2022.140785

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

automatic forecasting; correlogram; Mann–Kendall test; stationarity test; time series; unit root; XLSTAT

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2022
• Tom: no. 53
• Strony: 100--107
• Opis fizyczny: Bibliogr. 29 poz., tab., wykr.

Twórcy

autor

Tadesse Kassahun Birhanu

• University of Johannesburg, Faculty of Engineering and the Built Environment, Department of Civil Engineering Sciences, PO Box 524, Auckland Park, 2006 Johannesburg, South Africa

• https://orcid.org/0000-0002-7355-7067

autor

Dinka Megersa Olumana

• University of Johannesburg, Faculty of Engineering and the Built Environment, Department of Civil Engineering Sciences, PO Box 524, Auckland Park, 2006 Johannesburg, South Africa

• https://orcid.org/0000-0003-3032-7672

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).