Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches
- 418 Downloads
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.
KeywordsLarge-scale climate predictors Classification and regression trees Machine learning Semiarid region
This study was partially funded by University of Tehran (grant number 7401001/1/4).
- Akaike H (1998) Information theory and an extension of the maximum likelihood principle. In: Selected papers of Hirotugu Akaike. Springer, New York, pp 199–213. https://doi.org/10.1007/978-1-4612-1694-0_15
- Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth and Brooks/Cole, MontereyGoogle Scholar
- Choubin B, Khalighi-Sigaroodi S, Malekian A, Kişi Ö (2016a) Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrol Sci J 61(6):1001–1009. https://doi.org/10.1080/02626667.2014.966721 CrossRefGoogle Scholar
- Fallah-Ghalhary GA, Habibi-Nokhandan M, Mousavi-Baygi M, Khoshhal J, Shaemi Barzoki A (2010) Spring rainfall prediction based on remote linkage controlling using adaptive neuro-fuzzy inference system (ANFIS). Theoret Appl Climatol 101:217–233. https://doi.org/10.1007/s00704-009-0194-x CrossRefGoogle Scholar
- Khalili A (1997) Integrated water plan of Iran. Vol. 4: meteorological studies, ministry of energy, Iran. Lecha, L. and P. Shackleford, 1997. Climate services for tourism and recreation. WMO Bull 46:46–47Google Scholar
- Nazemosadat MJ, Cordey I (2000) On the relationship between ENSO and autumn rainfall in Iran. Int J Climatol 20(1):47–61. https://doi.org/10.1002/(sici)1097-0088(200001)20:1<47::aid-joc461>3.0.co;2-pGoogle Scholar
- Singh J, Knapp HV, Arnold JG, Demissie M (2005) Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. J Am Water Resour Assoc 41(2):343–360. https://doi.org/10.1111/j.1752-1688.2005.tb03740.x CrossRefGoogle Scholar
- Singh R, Wagener T, Crane R, Mann ME, Ning L (2014) A vulnerability driven approach to identify adverse climate and land use change combinations for critical hydrologic indicator thresholds: application to a watershed in Pennsylvania, USA. Water Resour Res 50:3409–3427. https://doi.org/10.1002/2013WR014988 CrossRefGoogle Scholar
- Timofeev R (2004) Classification and regression trees (CART) theory and applications. In: Master Thesis. Center of Applied Statistics and Economics, Humboldt University, BerlinGoogle Scholar