Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand)

Table 1

Details and structures of WANN model.

Structure	Model	Learning rule	Transfer function	Momentum	Maximum epochs	Runs	Processing elements	Hidden layers
D1	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D2	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D3	MLP	momentum	Tan Axon	0.8	1000	3	50	1
D4	MLP	momentum	Tan Axon	0.6	1000	3	50	1
D5	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D6	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D7	MLP	momentum	Tan Axon	0.8	1000	3	50	1
D8	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D9	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D10	MLP	momentum	Tan Axon	0.9	1000	3	50	1
D11	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D12	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D13	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D14	MLP	momentum	Tan Axon	0.6	1000	3	50	1
D15	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D16	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D17	MLP	momentum	Tan Axon	0.7	1000	3	50	1
D18	MLP	momentum	Tan Axon	0.7	1000	3	50	1