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Abstract
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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 difcult to make due to short-term extremes and data
scarcity. The current research is a new application of classifcation 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–Sutclife efciency, 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 fash food
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