Machine Learning Models for Spring Discharge Forecasting

Table 1

Comparative analysis of M5P, RF, and SVR by means of , MAE, RMSE, and RAE.

	Model		MAE (m^3/s)	RMSE (m^3/s)	RAE
4-month input	M5P	0.991	0.0124	0.0156	14.97%
	RF	0.926	0.0309	0.0446	37.29%
	SVR	0.97	0.0196	0.0299	23.67%
6-month input	M5P	0.987	0.013	0.018	15.67%
	RF	0.963	0.0261	0.035	31.50%
	SVR	0.976	0.0191	0.0291	22.97%
8-month input	M5P	0.889	0.0214	0.0312	41.24%
	RF	0.823	0.0297	0.0377	57.26%
	SVR	0.86	0.0275	0.0348	52.96%
4-month input	M5P	0.962	0.0272	0.0309	32.50%
	RF	0.972	0.0322	0.0391	38.53%
	SVR	0.933	0.03	0.0402	35.91%
6-month input	M5P	0.976	0.0207	0.026	24.73%
	RF	0.972	0.0333	0.0389	39.81%
	SVR	0.95	0.028	0.0369	33.55%
8-month input	M5P	0.675	0.0484	0.0623	75.87%
	RF	0.834	0.0398	0.0491	62.24%
	SVR	0.84	0.0381	0.0489	59.61%
4-month input	M5P	0.859	0.0487	0.0544	56.43%
	RF	0.964	0.0373	0.0435	43.12%
	SVR	0.791	0.0507	0.0637	58.69%
6-month input	M5P	0.921	0.0349	0.0405	40.40%
	RF	0.96	0.0388	0.0475	44.88%
	SVR	0.838	0.0464	0.0589	53.65%
8-month input	M5P	0.586	0.048	0.0591	84.85%
	RF	0.855	0.0409	0.0496	72.37%
	SVR	0.389	0.0638	0.0682	112.87%
4-month input	M5P	0.755	0.0544	0.0612	71.83%
	RF	0.936	0.0359	0.0393	47.41%
	SVR	0.731	0.0528	0.0621	69.83%
6-month input	M5P	0.831	0.0449	0.051	59.39%
	RF	0.945	0.0373	0.0441	49.25%
	SVR	0.857	0.0415	0.05	54.88%
8-month input	M5P	0.709	0.0379	0.0475	70.49%
	RF	0.934	0.0277	0.0351	51.44%
	SVR	0.797	0.0314	0.0406	58.39%