Journal of Big Data

Table 10 Results of ICN airport

From: Prediction of flight departure delays caused by weather conditions adopting data-driven approaches

Algorithm		Time difference: 2 h						Time difference: 4 h
Algorithm		Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)	Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)
DT	Normal	0.688	0.704	0.676	0.690	0.112	0.318	0.681	0.693	0.681	0.687	0.099	0.318
	Delayed		0.671	0.700	0.685				0.669	0.681	0.675
RF	Normal	0.749	0.729	0.814	0.769	2.254	16.242	0.735	0.717	0.802	0.757	2.231	16.242
	Delayed		0.776	0.680	0.725				0.760	0.665	0.710
SVM	Normal	0.651	0.631	0.774	0.695	3.625	458.280	0.646	0.629	0.756	0.687	3.452	474.522
	Delayed		0.686	0.522	0.593				0.672	0.529	0.592
KNN	Normal	0.641	0.655	0.637	0.646	0.003	60.510	0.652	0.662	0.661	0.661	0.004	31.847
	Delayed		0.628	0.646	0.637				0.642	0.643	0.643
LR	Normal	0.595	0.600	0.635	0.617	0.085	0.318	0.583	0.591	0.613	0.602	0.094	0.637
	Delayed		0.589	0.552	0.570				0.575	0.553	0.563
XGB	Normal	0.721	0.715	0.759	0.736	0.150	1.274	0.707	0.700	0.753	0.725	0.125	1.274
	Delayed		0.728	0.680	0.703				0.716	0.659	0.686
LSTM	Normal	0.644	0.620	0.776	0.689	490.4	3.503	0.609	0.602	0.679	0.638	490.8	0.318
	Delayed		0.687	0.509	0.584				0.618	0.537	0.575

Algorithm		Time difference: 8 h						Time difference: 16 h
Algorithm		Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)	Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)
DT	Normal	0.678	0.691	0.675	0.683	0.095	0.318	0.687	0.702	0.678	0.690	0.091	0.318
	Delayed		0.665	0.681	0.673				0.672	0.696	0.684
RF	Normal	0.744	0.726	0.806	0.764	2.187	16.242	0.745	0.719	0.826	0.769	2.136	16.242
	Delayed		0.768	0.679	0.721				0.782	0.659	0.715
SVM	Normal	0.641	0.625	0.750	0.682	3.591	530.255	0.641	0.626	0.749	0.682	3.595	545.541
	Delayed		0.666	0.525	0.587				0.666	0.528	0.589
KNN	Normal	0.662	0.673	0.666	0.669	0.003	30.892	0.649	0.663	0.643	0.653	0.004	32.803
	Delayed		0.651	0.658	0.655				0.635	0.656	0.645
LR	Normal	0.598	0.612	0.591	0.601	0.095	0.318	0.525	0.535	0.574	0.554	0.069	0.637
	Delayed		0.583	0.605	0.594				0.512	0.472	0.491
XGB	Normal	0.727	0.714	0.784	0.747	0.123	0.955	0.714	0.706	0.759	0.732	0.123	1.274
	Delayed		0.745	0.668	0.704				0.724	0.665	0.693
LSTM	Normal	0.587	0.581	0.669	0.622	488.4	0.318	0.540	0.531	0.797	0.637	490.1	3.503
	Delayed		0.595	0.502	0.545				0.566	0.274	0.369

Algorithm		Time difference: 24 h						Time difference: 48 h
Algorithm		Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)	Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)
DT	Normal	0.676	0.704	0.672	0.688	0.093	0.637	0.7680	0.692	0.679	0.685	0.102	0.637
	Delayed		0.670	0.702	0.685				0.668	0.681	0.674
RF	Normal	0.743	0.724	0.808	0.764	2.230	16.561	0.748	0.721	0.830	0.772	2.465	19.427
	Delayed		0.769	0.674	0.718				0.786	0.661	0.718
SVM	Normal	0.647	0.625	0.784	0.695	3.922	572.930	0.631	0.619	0.732	0.671	3.846	592.357
	Delayed		0.688	0.502	0.581				0.650	0.525	0.580
KNN	Normal	0.651	0.652	0.651	0.005	31.529	0.648	0.660	0.650	0.655	0.004	35.032	0.641
	Delayed		0.632	0.630	0.631				0.636	0.646	0.641
LR	Normal	0.547	0.564	0.528	0.545	0.089	0.637	0.554	0.565	0.574	0.569	0.098	0.637
	Delayed		0.533	0.568	0.550				0.542	0.532	0.537
XGB	Normal	0.705	0.702	0.739	0.720	0.150	1.274	0.703	0.693	0.758	0.724	0.138	1.274
	Delayed		0.708	0.669	0.688				0.716	0.646	0.679
LSTM	Normal	0.580	0.586	0.591	0.588	493.3	3.185	0.551	0.548	0.666	0.601	494.4	3.503
	Delayed		0.574	0.569	0.572				0.556	0.433	0.487

Bold values indicate the greatest results

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