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 4 Comparison of Accuracy 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	0.949	0.890	0.859	0.789	0.759	0.731	0.717	0.707	0.699	0.695	0.690	0.680
	MLP	0.958	0.910	0.890	0.886	0.805	0.778	0.748	0.739	0.723	0.715	0.699	0.690
	RAQP	0.933	0.925	0.912	0.903	0.880	0.880	0.862	0.775	0.740	0.739	0.725	0.712
	Vlacho	0.867	0.828	0.792	0.770	0.748	0.719	0.651	0.636	0.621	0.615	0.605	0.590
	LSTM	0.938	0.928	0.919	0.890	0.860	0.832	0.803	0.791	0.770	0.718	0.712	0.700
	BLSTM	0.918	0.903	0.899	0.898	0.859	0.835	0.825	0.770	0.739	0.718	0.713	0.705
	SLSTM	0.969	0.965	0.935	0.912	0.883	0.853	0.833	0.815	0.790	0.758	0.750	0.721
	BlaSt	0.995	0.985	0.920	0.900	0.900	0.890	0.888	0.868	0.856	0.857	0.835	0.829
\(NO_{2}\)	SVR	0.930	0.925	0.910	0.898	0.893	0.883	0.873	0.867	0.853	0.808	0.797	0.788
	MLP	0.935	0.870	0.848	0.842	0.809	0.785	0.769	0.759	0.729	0.710	0.700	0.693
	RAQP	0.966	0.952	0.938	0.910	0.894	0.877	0.862	0.838	0.799	0.788	0.758	0.708
	Vlacho	0.888	0.859	0.850	0.845	0.833	0.812	0.802	0.795	0.759	0.730	0.723	0.713
	LSTM	0.962	0.956	0.945	0.915	0.887	0.866	0.852	0.790	0.770	0.761	0.747	0.728
	BLSTM	0.987	0.964	0.940	0.915	0.894	0.865	0.847	0.810	0.799	0.770	0.756	0.728
	SLSTM	0.980	0.978	0.960	0.935	0.893	0.846	0.842	0.820	0.799	0.770	0.757	0.740
	BlaSt	0.995	0.990	0.892	0.898	0.897	0.869	0.860	0.849	0.845	0.839	0.839	0.830
CO	SVR	0.935	0.890	0.889	0.864	0.844	0.818	0.788	0.772	0.760	0.703	0.688	0.680
	MLP	0.955	0.950	0.940	0.885	0.868	0.838	0.808	0.782	0.765	0.742	0.735	0.723
	RAQP	0.962	0.943	0.910	0.895	0.883	0.863	0.847	0.829	0.812	0.784	0.760	0.748
	Vlacho	0.870	0.869	0.865	0.860	0.859	0.847	0.839	0.821	0.813	0.802	0.790	0.782
	LSTM	0.975	0.944	0.910	0.863	0.853	0.830	0.797	0.760	0.733	0.730	0.727	0.713
	BLSTM	0.991	0.964	0.940	0.908	0.894	0.866	0.827	0.810	0.790	0.770	0.747	0.718
	SLSTM	0.992	0.957	0.938	0.910	0.868	0.849	0.827	0.795	0.765	0.735	0.727	0.710
	BlaSt	0.996	0.968	0.925	0.907	0.899	0.885	0.879	0.869	0.859	0.849	0.839	0.835

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

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