Estimation of Air Pollutant Transportation Equation in Surabaya using Kalman Filter Method

Didik Khusnul Arif, Helisyah Nur Fadhilah, Prima Aditya

Abstract


Surabaya is one of big cities in Indonesia. Since the number of industries in Surabaya is increasing, the level of air pollution in Surabaya is also increasing. To deal with this matter, procurement of measuring instruments has been carried out in several locations in Surabaya based on the level of emissions issued by motorized vehicles, but increasing the number of measuring instruments affects the cost significantly. Therefore, the estimation of air pollution will make it easier to determine the level of air pollution in Indonesia. Kalman filter a method to estimate the state variable by using a system model accompanied by a measurement model as the initial value that comprises the prediction stage and the correction stage. The results of this correction stage will be the estimation results. Then it will be compared with the data at some locations. The results obtained are quite accurate at the point of observation with a relatively small error.

Keywords


Mathematics; Estimation; Computing

Full Text:

PDF

References


K. Aisyiah, S. Sutikno, and I. Latra, “Pemodelan konsentrasi partikel debu (pm10) pada pencemaran udara di kota surabaya dengan metode geographically-temporally weighted regression,” Jurnal Sains dan Seni ITS, vol. 3, no. 2, pp. D152–D157, 2014.

Y. Oktaviana, E. Apriliani, and D. Arif, “Fractional kalman filter to estimate the concentration of air pollution,” in Journal of Physics: Conference Series, vol. 1008, no. 1, 2018, p. 012008.

D. Arif, D. Adzkiya, P. Aditya, F. Winata, D. Agustin, M. Habibi, A. Ririsati, and R. Prasyanto, “Modeling of three-dimensional radar tracking system and its estimation using extended kalman filter,” in 5th International Conference on Instrumentation, Control, and Automation (ICA), 2017, pp. 51–55.

V. Rachmawati, D. Arif, and D. Adzkiya, “Implementation of Kalman filter algorithm on models reduced using singular pertubation approximation method and its application to measurement of water level,” in Journal of Physics: Conference Series, vol. 974, no. 1, 2018, p. 012018.

R. Fitria and D. Arif, “State variable estimation of nonisothermal continuous stirred tank reactor using fuzzy kalman filter,” International Journal of Computing Science and Applied Mathematics, vol. 3, no. 1, pp. 16–20, 2017.

E. Purnaningrum and E. Apriliani, “Auto floodgate control using enkf-nmpc method,” International Journal of Computing Science and Applied Mathematics, vol. 2, no. 1, pp. 14–19, 2016.

P. Aditya, E. Apriliani, G. Zhai, and D. Arif, “Formation control of multirobot motion systems and state estimation using extended kalman filter,” in International Conference on Electrical Engineering and Informatics (ICEEI), 2019, pp. 99–104.

D. Arif, D. Adzkiya, E. Apriliani, and I. Khasanah, “Model reduction of non-minimal discrete-time linear-time-invariant systems,” Malaysian Journal of Mathematical Sciences, vol. 11, no. 3, pp. 377–391, 2017.

D. Arif, Widodo, Salmah, and E. Apriliani, “Construction of the Kalman filter algorithm on the model reduction,” International Journal of control and Automation, vol. 7, no. 9, pp. 257–270, 2014.




DOI: http://dx.doi.org/10.12962/j24775401.v6i1.4662

Refbacks

  • There are currently no refbacks.



View My Stats


Creative Commons License
International Journal of Computing Science and Applied Mathematics by Pusat Publikasi Ilmiah LPPM, Institut Teknologi Sepuluh Nopember is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/ijcsam.