Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

Table 3 Alternative recurrent neural network models for the three cities

City	Model	Learning rate	Dimensions of hidden layer	Number of epochs	MAPE (%)^a	MAPE (%)^b	MAPE (%)^c
Xuzhou	RNN1	0.05	3	500	16.14	15.99	16.46
	RNN2	0.05	3	500	13.42	13.30	14.41
	RNN3	0.2	3	150	13.08	11.95	12.07
	RNN4	0.05	3	600	10.33	10.33	10.40
	RNN5	0.05	5	600	8.45	8.25	8.54
	RNN6 (RNN5 + MAS1)	0.05	3	1000	7.36	7.33	7.33
	RNN7 (RNN5 + MAS2 + MST2)	0.05	3	800	6.38	6.31	6.42
	RNN8 (RNN5 + MAT3 + MAS3 + MP3 + MST3)	0.05	5	600	4.78	4.89	4.97
	RNN9 (RNN5 + MAS1 + MAS2 + MST2 + MAT3 + MAS3 + MP3 + MST3)	0.05	10	600	5.75	5.40	5.90
Nantong	RNN1	0.05	3	500	21.91	21.99	21.78
	RNN2	0.2	5	80	16.92	17.81	16.31
	RNN3	0.2	3	150	13.82	14.26	13.86
	RNN4	0.2	3	150	12.78	12.84	12.80
	RNN5	0.2	5	100	11.38	11.44	11.24
	RNN6 (RNN5 + MAS1 + MAH1)	0.05	5	1000	9.19	8.82	8.84
	RNN7 (RNN5 + MAS2 + MAH2)	0.05	5	1000	8.58	8.26	8.52
	RNN8 (RNN5 + MAS3 + MAH3)	0.05	10	800	8.87	8.79	8.69
	RNN9 (RNN5 + MAS1 + MAH1 + MAS2 + MAH2 + MAS3 + MAH3)	0.05	5	800	8.79	9.21	9.19
Wuxi	RNN1	0.1	10	150	23.76	23.81	23.77
	RNN2	0.05	5	400	19.93	19.54	20.17
	RNN3	0.05	10	250	18.23	17.84	18.59
	RNN4	0.05	10	400	17.15	17.40	17.31
	RNN5	0.05	5	600	14.10	13.93	13.95
	RNN6 (RNN5 + MAT1 + MAP1 + MAS1 + MAH1 + MST1)	0.05	3	1500	13.01	13.39	13.04
	RNN7 (RNN5 + MAS2)	0.1	5	800	12.62	12.36	12.80
	RNN8 (RNN5 + MAT3 + MAS3 + MAH3)	0.05	10	1000	12.71	13.06	12.94
	RNN9 (RNN5 + MAT1 + MAP1 + MAS1 + MAH1 + MST1 + MAS2 + MAT3 + MAS3 + MAH3)	0.1	3	1000	12.81	12.80	13.46

RNN recurrent neural network, MAPE mean absolute percentage error, MAT monthly average temperature, MAP monthly average atmospheric pressure, MAS monthly average wind speed, MAH monthly average relative humidity, MP monthly precipitation, MST monthly sunshine time, 1 1 month prior, 2 2 months prior, 3 3 months prior
^a MAPE of the model with the testing set after the first training
^b MAPE of the model with the testing set after the second training
^c MAPE of the model with the testing set after the third training

ISSN: 2049-9957