Drought forecasting using new advanced ensemble-based models of reduced error pruning tree

• Autorzy: Shahdad Mojtaba , Saber Behzad

Identyfikatory

DOI

10.1007/s11600-022-00738-2

• Języki publikacji: EN

Abstrakty

EN

The present study investigates the prediction accuracy of standalone Reduced Error Pruning Tree model and its integration with Bagging (BA), Dagging (DA), Additive Regression (AR) and Random Committee (RC) for drought forecasting on time scales of 3, 6, 12, 48 months ahead using Standard Precipitation Index (SPI), which is among the most common criteria for testing drought prediction, at Kermanshah synoptic station in western Iran. To this end, monthly data obtained from a 31-year period record including rainfall, maximum and minimum temperatures, and maximum and minimum relative humidtty rates were considered as the required input to predict SPI. In addition, different inputs were combined and constructed to determine the most effective parameter. Finally, the obtained results were validated using visual and quantitative criteria. According to the results, the best input combination comprised both meteorological variable and SPI along with lag time. Although hybrid models enhanced the results of standalone models, the accuracy of the best performing models could vary on different SPI time scales. Overall, BA, DA and RC models were much more effective than AR models. Moreover, RMSE value increased from SPI (3) to SPI (48), indicating that performance modeling would become much more challenging and complex on higher time scales. Finally, the performance of the newly developed models was compared with that of conventional and most commonly used Support Vector Machine and Adaptive Neuro-Fuzzy Inference System (ANFIS) models, regarded as the benchmark. The results revealed that all the newly developed models were characterized by higher prediction power than ANFIS and ANN.

Słowa kluczowe

EN

drought forecasting; machine learning; ensemble model; REP tree

PL

prognozowanie suszy; nauczanie maszynowe; model regresji

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 2
• Strony: 697--712
• Opis fizyczny: Bibliogr. 103 poz.

Twórcy

autor

Shahdad Mojtaba

• Engineering and Management of Water Resources, Department of Civil Engineering, Maragheh Branch, Islamic Azad University, Maragheh, Iran

• https://orcid.org/0000-0002-1186-431X

autor

Saber Behzad

• Civil Engineering, Department of Civil Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

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Uwagi

PL

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).