Ecosense: a revolution in urban air quality forecasting for smart cities

Chatterjee, Kalyan; Thara, Machakanti Navya; Reddy, Mandadi Sriya; Selvamuthukumaran, N.; Priyadharshini, M.; Reddy, Tummala Abhinav Vardhan; Chakraborty, Somenath; Mallik, Saurav; Shah, Mohd Asif; Li, Aimin

doi:10.1186/s13104-025-07099-1

BMC Research Notes

Table 6 Comparison of Precision between SVR [31], MLP [32], RAQP [34], Vlachogianni (Vlacho) [4], LSTM [35], BLSTM [36], SLSTM [37], and the proposed BlaSt Models

From: Ecosense: a revolution in urban air quality forecasting for smart cities

APCs	Method	1hr	2hr	3hr	4hr	5hr	6hr	7hr	8hr	9hr	10hr	11hr	12hr
\(PM_{2.5}\)	SVR	0.938	0.904	0.892	0.862	0.802	0.798	0.753	0.735	0.710	0.697	0.655	0.633
	MLP	0.958	0.927	0.895	0.856	0.823	0.790	0.770	0.758	0.733	0.714	0.710	0.703
	RAQP	0.920	0.912	0.904	0.890	0.879	0.853	0.839	0.790	0.768	0.746	0.713	0.699
	Vlacho	0.867	0.844	0.813	0.785	0.734	0.716	0.687	0.650	0.629	0.610	0.605	0.599
	LSTM	0.948	0.923	0.919	0.893	0.863	0.833	0.813	0.793	0.753	0.740	0.725	0.711
	BLSTM	0.966	0.920	0.890	0.865	0.843	0.825	0.815	0.793	0.773	0.743	0.723	0.703
	SLSTM	0.979	0.954	0.934	0.913	0.880	0.863	0.825	0.801	0.790	0.765	0.743	0.723
	BlaSt	0.995	0.975	0.930	0.910	0.896	0.892	0.880	0.860	0.860	0.850	0.840	0.828
\(NO_{2}\)	SVR	0.915	0.913	0.903	0.888	0.883	0.873	0.863	0.857	0.843	0.798	0.783	0.778
	MLP	0.900	0.878	0.838	0.832	0.808	0.783	0.760	0.753	0.724	0.720	0.703	0.701
	RAQP	0.966	0.958	0.928	0.910	0.884	0.867	0.839	0.828	0.798	0.763	0.748	0.707
	Vlacho	0.868	0.848	0.840	0.835	0.823	0.802	0.797	0.790	0.720	0.709	0.705	0.703
	LSTM	0.963	0.943	0.940	0.905	0.884	0.856	0.847	0.784	0.760	0.751	0.737	0.718
	BLSTM	0.975	0.954	0.930	0.905	0.884	0.857	0.837	0.800	0.789	0.760	0.746	0.718
	SLSTM	0.980	0.978	0.950	0.925	0.883	0.856	0.837	0.810	0.790	0.772	0.747	0.728
	BlaSt	0.992	0.980	0.903	0.889	0.884	0.874	0.865	0.858	0.848	0.843	0.838	0.836
CO	SVR	0.923	0.889	0.869	0.854	0.834	0.808	0.778	0.762	0.720	0.697	0.688	0.678
	MLP	0.952	0.946	0.888	0.858	0.828	0.798	0.778	0.760	0.739	0.730	0.712	0.706
	RAQP	0.960	0.933	0.898	0.898	0.893	0.873	0.869	0.865	0.852	0.843	0.820	0.778
	Vlacho	0.872	0.867	0.863	0.860	0.859	0.857	0.849	0.841	0.813	0.804	0.790	0.784
	LSTM	0.973	0.934	0.900	0.875	0.843	0.820	0.787	0.750	0.738	0.722	0.717	0.703
	BLSTM	0.989	0.954	0.930	0.905	0.884	0.856	0.817	0.800	0.780	0.760	0.737	0.708
	SLSTM	0.980	0.954	0.933	0.908	0.864	0.839	0.817	0.790	0.760	0.730	0.717	0.701
	BlaSt	0.995	0.965	0.925	0.897	0.896	0.886	0.878	0.865	0.857	0.847	0.836	0.834

Bold values indicate the efficiency as well as the superior performance of our proposed model

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