Multiple linear regression,multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals |
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| Authors: | Bahram Choubin Shahram Khalighi-Sigaroodi Özgür Kişi |
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| Affiliation: | 1. Department of Watershed Management and Engineering, Sari University of Agriculture Sciences and Natural Resources, Sari, Iran;2. Department of Watershed Science and Engineering, University of Tehran, Karaj, Iran;3. Department of Civil Engineering, University of Canik Basari, Samsun, Turkey |
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| Abstract: | ABSTRACTNowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared for precipitation forecasting. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals on the seasonal standardized precipitation index (SPI) of the Maharlu-Bakhtaran catchment, Iran, simultaneously and with a delay, was analysed using a cross-correlation function. Hence, the SPI time series was forecasted up to four time intervals using MLR, MLP and ANFIS. The results showed that most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance for the MLP network than the other two models (RMSE = 0.86, MAE = 0.74 for the first step ahead of SPI forecasting). Editor D. Koutsoyiannis; Associate editor F. Pappenberger |
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| Keywords: | climate signals standardized precipitation index multiple linear regression adaptive neuro-fuzzy inference system multi-layer perceptron network |
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