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Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches

  • Bahram ChoubinEmail author
  • Gholamreza Zehtabian
  • Ali Azareh
  • Elham Rafiei-Sardooi
  • Farzaneh Sajedi-Hosseini
  • Özgür Kişi
Original Article

Abstract

Interest in semiarid climate forecasting has prominently grown due to risks associated with above average levels of precipitation amount. Longer-lead forecasts in semiarid watersheds are difficult to make due to short-term extremes and data scarcity. The current research is a new application of classification and regression trees (CART) model, which is rule-based algorithm, for prediction of the precipitation over a highly complex semiarid climate system using climate signals. We also aimed to compare the accuracy of the CART model with two most commonly applied models including time series modeling (ARIMA), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of the precipitation. Various combinations of large-scale climate signals were considered as inputs. The results indicated that the CART model had a better results (with Nash–Sutcliffe efficiency, NSE > 0.75) compared to the ANFIS and ARIMA in forecasting precipitation. Also, the results demonstrated that the ANFIS method can predict the precipitation values more accurately than the time series model based on various performance criteria. Further, fall forecasts ranked “very good” for the CART method, while the ANFIS and the time series model approximately indicated “satisfactory” and “unsatisfactory” performances for all stations, respectively. The forecasts from the CART approach can be helpful and critical for decision makers when precipitation forecast heralds a prolonged drought or flash flood.

Keywords

Large-scale climate predictors Classification and regression trees Machine learning Semiarid region 

Notes

Acknowledgements

This study was partially funded by University of Tehran (grant number 7401001/1/4).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Watershed ManagementSari Agricultural Sciences and Natural Resources UniversitySariIran
  2. 2.Department of Reclamation of Arid and Mountainous RegionsUniversity of TehranKarajIran
  3. 3.Department of GeographyUniversity of JiroftKermanIran
  4. 4.Faculty of Natural ResourcesUniversity of JiroftKermanIran
  5. 5.Faculty of Natural Sciences and EngineeringIlia State UniversityTbilisiGeorgia

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