Issue

Vol. 5, No. 1, February 2020

Prediction of Biochemical Oxygen Demand Using Radial Basis Function Network

https://doi.org/10.22219/kinetik.v5i1.1006
Muhammad Noor
Institut Teknologi Sepuluh Nopember
Joko Lianto Buliali
Institut Teknologi Sepuluh Nopember

Corresponding Author(s) : Muhammad Noor

muhammadnoor.18051@mhs.its.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 1, February 2020

Abstract

Biochemical oxygen demand shows the amount of oxygen needed by microorganisms to decompose dissolved organic substances suspended in water. This variable determines water quality. The higher value indicates lower water quality. Obtaining this value requires a lengthy procedure of five days in typical laboratories. This paper proposes to predict biochemical oxygen demand using a radial basis function network with improvement relational fuzzy c-means clustering to set centroid by using 11 parameters that come from water quality records. The dataset used in testing consisting of weekly parameters between 2014-2019. Testing results show performance measurement of mean absolute error, mean square error, root mean square error, mean absolute percentage error, and accuracy using centroid with improvement relational fuzzy c-means 0.15016, 0.3677, 0.19082, 21.64490 and 78.35510 comparing with centroid from fuzzy c-means 0.16002, 0.04021, 0.19963, 22.83184, and 77.16816.

Keywords

Water Quality Biochemical Oxygen Demand (BOD) Prediction Improvement Relational Fuzzy c-Means Clustering (iRFCM) Radial Basis Function Network (RBFN)
Noor, M., & Buliali, J. L. (2020). Prediction of Biochemical Oxygen Demand Using Radial Basis Function Network. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(1), 21-28. https://doi.org/10.22219/kinetik.v5i1.1006
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References
  1. P. R. Indonesia, “PP No. 82 Tahun 2001 Tentang Pengelolaan Kualitas Air dan Pengendalian Pencemaran Air,” 2001.
  2. R. Noori, S. Safavi, and S. A. Nateghi Shahrokni, “A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand,” J. Hydrol., vol. 495, pp. 175–185, 2013. https://doi.org/10.1016/j.jhydrol.2013.04.052
  3. A. A. M. Ahmed and S. M. A. Shah, “Application of Adaptive Neuro-Fuzzy Inference System ( ANFIS ) to Estimate the Biochemical Oxygen Demand ( BOD ) of Surma River,” J. King Saud Univ. - Eng. Sci., vol. 29, pp. 237–243, 2017. https://doi.org/10.1016/j.jksues.2015.02.001
  4. Salmin, “Oksigen Terlarut (DO) Dan Kebutuhan Oksigen Biologi (BOD) Sebagai Salah Satu Indikator Untuk Menentukan Kualitas Perairan,” Oseana, vol. 30, no. 3, pp. 21–26, 2005.
  5. A. Solgi, A. Pourhaghi, R. Bahmani, and H. Zarei, “Improving SVR and ANFIS performance using wavelet transform and PCA algorithm for modeling and predicting biochemical oxygen demand (BOD),” Ecohydrol. Hydrobiol., vol. 17, no. 2, pp. 164–175, 2017. https://doi.org/10.1016/j.ecohyd.2017.02.002
  6. Nasional Badan Standardisasi, “SNI 6968.72:2009 Air dan Air Limbah : Cara Uji Kebutuhan Oksigen Biokimia (Biochemical Oxygen Demand/BOD),” 2009.
  7. L. Fanjun, Q. Junfei, and Z. Wei, “A Fast Growing Cascade Neural Network for BOD Estimation,” 2015 34th Chinese Control Conf., vol. 2015, pp. 3417–3422, 2015. https://doi.org/10.1109/ChiCC.2015.7260167
  8. J. Qiao, W. Li, and H. Han, “Soft computing of biochemical oxygen demand using an improved T-S fuzzy neural network,” Chinese J. Chem. Eng., vol. 22, no. 11, pp. 1254–1259, 2014. https://doi.org/10.1016/j.cjche.2014.09.023
  9. E. R. Rene and M. B. Saidutta, “Prediction of bod and cod of a refinery wastewater using multilayer artificial neural networks,” J. Urban Environ. Eng., vol. 2, no. 1, pp. 1–7, 2008. https://doi.org/10.4090/juee.2008.v2n1.001007
  10. X. Li and J. Song, “A New ANN-Markov chain methodology for water quality prediction,” Proc. Int. Jt. Conf. Neural Networks, 2015. https://doi.org/10.1109/IJCNN.2015.7280320
  11. D. S. Broomhead and D. Lowe, “Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks,” R. Signals Radar Establ., no. 4148, 1988.
  12. C. Wang and C. Yan, “Comparison of Four Kinds of Fuzzy C-means Clustering Methods and Their Applications on Posture Classification,” Proc. Int. Symp. Intell. Inf. Syst. Appl., vol. 6, no. 2, pp. 382–385, 2009. https://doi.org/10.1109/ISIP.2010.133
  13. R. J. Hathaway, J. W. Davenport, and J. C. Bezdek, “Relational Duals of The C-Means Clustering Algorithms,” Pattern Recognit., vol. 22, no. 2, pp. 205–212, 1989. https://doi.org/10.1016/0031-3203(89)90066-6
  14. Z. Mustaffa and Y. Yusof, “A Comparison of Normalization Techniques in Predicting Dengue Outbreaik,” Int. Conf. Bus. Econ. Res., vol. 1, pp. 345–349, 2011.
  15. D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN,” Comput. Eng. Sci. Syst. J., vol. 4, no. 1, pp. 78–82, 2019. https://doi.org/10.24114/cess.v4i1.11458
  16. M. Stone, “Cross-Validatory Choice and Assessment of Statistical Predictions,” J. R. Stat. Soc. Ser. B, vol. 36, no. 2, pp. 111–133, 1974.
  17. R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” Appear. Int. Jt. Conf. Artif. Intell., vol. 118, no. 4, pp. 456–461, 1995.
  18. M. A. Banjarsari, H. I. Budiman, and A. Farmadi, “Penerapan K-Optimal Pada Algoritma Knn untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Ilmu Komputer Fmipa Unlam Berdasarkan IP Sampai Dengan Semester 4,” Kumpul. J. Ilmu Komput., vol. 02, no. 02, pp. 50–64, 2015. http://dx.doi.org/10.20527/klik.v2i2.26
  19. M. A. Khalilia, J. Bezdek, M. Popescu, and J. M. Keller, “Improvements to the relational fuzzy c-means clustering algorithm,” Pattern Recognit., vol. 47, no. 12, pp. 3920–3930, 2014. https://doi.org/10.1016/j.patcog.2014.06.021
  20. R. J. Hathaway and J. C. Bezdek, “Nerf C-Means: Non-Euclidean Relational Fuzzy Clustering,” Pattern Recognit., vol. 27, no. 3, pp. 429–437, 1994. https://doi.org/10.1016/0031-3203(94)90119-8
  21. Y. Kanazawa, “Relational Fuzzy c-Means and Kernel Fuzzy c-Means Using a Quadratic Programming-Based Object-Wise β-Spread Transformation,” Proc. Fifth Int. Conf. KSE 2013, vol. 2, pp. 29–43, 2013. https://doi.org/10.1007/978-3-319-02821-7_5
  22. J. C. Bezdek, R. Ehrlich, and W. Full, “FCM : The Fuzzy C-Means Custering Algorithm,” Comput. Geosci., vol. 10, no. 2, pp. 191–198, 1984.
  23. M. J. L. Orr, “Introduction to Radial Basis Function Networks, Univ. Edinburgh, pp. 1–67, 1996. https://doi.org/10.1016/0098-3004(84)90020-7
  24. T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014. https://doi.org/10.3354/cr030079
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Author Biographies

Muhammad Noor, Institut Teknologi Sepuluh Nopember
Informatics Department
Joko Lianto Buliali, Institut Teknologi Sepuluh Nopember
Informatics Department
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