Prediction and Estimation of Pm10 and SO2 Concentrations in the Ambient Air At Vijayawada Station using Artificial Neural Networks Computing
Pamula Raja Kumari1, R V S D Sai Pavan Avisetty2, Praneeth Akkala3, K V V Subash4, K. Surya Manideep5, Polaiah Bojja6, Bandi Aruna7
1Pamula Raja Kumari, Department of Mathematics, Koneru Lakshmaiah Education Foundation Deemed to be University, Guntur (Andhra Pradesh), India.
2R V S D Sai Pavan Avisetty, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation Deemed to be University, Guntur (Andhra Pradesh), India.
3Praneeth Akkala, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation Deemed to be University, Guntur (Andhra Pradesh), India.
4K V V Subash, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation Deemed to be University, Guntur (Andhra Pradesh), India.
5K. Surya Manideep, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation Deemed to be University, Guntur (Andhra Pradesh), India.
6Polaiah Bojja, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation Deemed to be University, Guntur (Andhra Pradesh), India.
7Bandi Aruna, Department of Electronics and Computer Science Enineering, Koneru Lakshmaiah Education Foundation Deemed to be University, Guntur (Andhra Pradesh), India.
Manuscript received on 04 May 2019 | Revised Manuscript received on 16 May 2019 | Manuscript Published on 28 May 2019 | PP: 790-793 | Volume-7 Issue-6C2 April 2019 | Retrieval Number: F11460476C219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The point of this paper is to use Artificial Neural Networks and Fuzzy Logic for measuring and foreseeing of vital contamination parameters that is SO2 and PM10 fixations. Structure and advancement of soft computing specialized methodologies like Feed-forward Back Propagation arrange (BPN) model and Mamdani Fuzzy Inference show which are prepared and tried utilizing five years past information (meteorological information). For improving the precision of estimation, assessing the base determining mistake (minimum forecast error) and the outcomes are done by utilizing MATLAB software. To contemplate the connection between meteorological parameters and PM10 by utilizing the previous authentic information to such an extent that they can be utilized for prediction of pollutant. To set up a model for the forecast of PM10 dependent on meteorological parameters at every single station by utilizing Artificial Neural Networks. To set up a model for the prediction of SO2 dependent on meteorological parameters including PM10 as one of the information parameters at every single station by utilizing Artificial Neural Networks. To set up a model for the forecast of PM10 and SO2 dependent on meteorological parameters at every single station by utilizing Artificial Neural Networks as a Computing techniques.
Keywords: Air Pollution, Artificial Neural-Networks, Expectation, Forecast, PM10, Sulfur Dioxide.
Scope of the Article: Soft Computing