The influence of rainfall time series fractality on forecasting models’ efficiency

Rahmani, Farhang; Fattahi, Mohammad Hadi

doi:10.1007/s11600-022-00776-w

Artykuł - szczegóły

Tytuł artykułu

The influence of rainfall time series fractality on forecasting models’ efficiency

Autorzy

Rahmani Farhang , Fattahi Mohammad Hadi

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00776-w

Warianty tytułu

Języki publikacji

Abstrakty

Forecasting rainfall time series is of great significance for hydrologists and geoscientists. Thus, this study represents a contribution to understanding the impact of the fractal time series variety on forecasting model performance. Multiple fractal series were generated via p-model and used for modeling. Subsequently, the forecasting was delivered based on existing observed monthly rainfall data (three stations in the UK, from 1865 to 2002) through five forecasting models. Finally, the association between series fractality and models’ performance was examined. The results indicated that the forecasting based on the mono-fractal series resulted in the most reliable results (R²=1 and RMSE less than 0.02). In the case of multifractal series, modeling based on series with the right side of the asymmetric curve of the multifractal spectrum presented series with the lowest RMSE (0.96) and highest R² (0.99) (desirable performance). In contrast, the forecasting based on series with the left side of the asymmetric curve of the multifractal spectrum suggested the most unreliable outcomes (R² range [−0.0007 ~ 0.988] and RMSE range [0.8526 ~ 39.3]). The forecasting based on the symmetric curve of the multifractal spectrum series delivered regular performance. Accordingly, high and low errors are expected from forecasting based on the time series with a left-skewed multifractal spectrum and right-skewed multifractal spectrum (and mono-fractal time series), respectively. Hybrid models were the best options for forecasting mono-fractal and multifractal time series with right side asymmetric and symmetric multifractal spectrum curves. The ARIMA model was suitable to predict multifractal time series with left side asymmetric multifractal spectrum curves.

Słowa kluczowe

ANFIS autoregressive integrated moving average ARIMA radial basis function RBF multifractal detrended fluctuation analysis MF-DFA hurst exponent multifractal spectrum

ANFIS autoregresyjna zintegrowana średnia ruchoma ARIMA radialna funkcja bazowa RBF multifraktalna beztrendowa analiza fluktuacyjna MF-DFA wykładnik Hursta widmo multifraktalne

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 3

Strony

1349--1361

Opis fizyczny

Bibliogr. 47 poz.

Twórcy

autor

Rahmani Farhang

farhang.rahmani@miau.ac.ir

Department of Civil Engineering, Marvdasht Branch Islamic Azad University, Marvdasht, Iran

autor

Fattahi Mohammad Hadi

Department of Civil Engineering, Marvdasht Branch Islamic Azad University, Marvdasht, Iran

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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).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-3091b1be-4ba7-4240-b713-3d548a9b5c1e