Araştırma Makalesi
BibTex RIS Kaynak Göster

DATA MINING PROCESS FOR RIVER SUSPENDED SEDIMENT ESTIMATION

Yıl 2016, Cilt: 8 Sayı: 3, 19 - 26, 01.12.2016

Özlem Terzi Tahsin Baykal

Öz

The accurate estimation of the amount of suspended sediment of rivers is important in water resources
engineering because sediment in rivers can also shorten the lifespan of dams and reservoirs. For this purpose, the
models are developed to estimate suspended sediment of Kızılırmak River using the data mining process. The
river flow values are used as input parameter by developing sediment models. The most appropriate model is
obtained by the M5’Rules algorithm. The determination coefficient of the model is obtained as 0.66 and it is
observed that the data mining process can be used to estimate suspended sediment of rivers in hydrology field.

Anahtar Kelimeler

sediment data mining process M5'Rules Kızılırmak River

Kaynakça

  • Braha, D., Shmilovici, A. (2002). Data mining for improving a cleaning process in the semiconductor industry. IEEE Transactions on Semiconductor Manufacturing. vol.15, 1.
  • Cunningham, S. J., Holmes, G. (1999). Developing innovative applications in agriculture using data mining. Proceedings of Southeast Asia Regional Computer Confederation Conference, Singapore.
  • Dogan, E. (2009). Prediction of sediment concentration using artificial neural Networks. Turkish Chamber of Civil Engineers, vol. 302, pp. 4567-4582.
  • Fayyad, U.M., Uthurusamy, R., (2002). Evolving data mining into solutions for insights. Communications of the ACM. vol. 45(8), pp. 28-31.
  • Hall , M.J., Minns, A.W., Ashrafuzzaman, A.K.M. (2002). The application of data mining techniques for the regionalisation of hydrological variables. Hydrology and Earth System Sciences. vol. 6(4), pp. 685-694.
  • Hall, M., Holmes, G., Frank, E. (1999). Generating Rule Sets from Model Trees. Proceedings of the Twelfth Australian Joint Conference on Artificial Intelligence. pp. 1-12., Sydney, Australia .
  • Heng, S. and Suetsugi, T. (2013). Using Artificial Neural Network to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River Basin, Cambodia. Journal of Water Resource and Protection, vol. 5(2), pp. 111-123.
  • Hoffmann, D., Apostolakis, J. (2003). Crystal Structure Prediction by Data Mining. Journal of Molecular Structure. vol. 647, pp. 17-39.
  • http://www.investopedia.com/ terms/m/mlr.asp.
  • http://homepages.inf.ed.ac.uk/jcavazos/SMART07/ paper_9_9.pdf.
  • Keskin, M. E., Taylan, D., Kucuksille, E. U. ( 2013). Data Mining Process for Modelling Hydrological Time Series. Hydrology Research. vol. 44 (1), pp. 78-88.
  • Keskin, M.E., Terzi, Ö., Küçüksille, E.U., (2009). Data mining process for integrated evaporation model. Journal of Irrigation and Drainage Engineering. vol. 135(1), pp. 39-43.
  • Li, S.T., Shue, L.Y., (2004). Data mining to aid policy making in air pollution management. Expert System and Applications. vol. 27, pp. 331-340.
  • Lin, C.T., Lee, C.S.G. (1995). Neural fuzzy systems. Prentice Hall.
  • Mattison, R. (1996). Data Warehousing: Strategies, Technologies and Techniques Statistical Analysis. SPSS Inc. WhitePapers.
  • Mirbagheri, S. A., Nourani., V., Rajaee, T., Alikhani, A.(2010). Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers.
  • Hydrological Sciences Journal – Journal des Sciences Hydrologiques. vol. 55(7), pp. 1175- 1189.
  • Mishra, S., Dwivedi, V. K., Saravanan, C., Pathak, K. K. (2013). Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin. International Journal of Computer Applications. vol. 67(6), pp. 7-14.
  • Rupp, B., Wang, J. (2004). Predictive Models For Protein Crystallization. Methods, vol. 34, pp. 390-407.
  • Terzi, Ö., (2011). Monthly River Flow Forecasting by Data Mining Process. KnowledgeOriented Applications in Data Mining, K. Funatsu (Ed.). InTech.
  • Terzi, Ö., Küçüksille, E.U., Ergin, G., İlker, A. (2011). Estimation of Solar Radiation Using Data Mining Process. SDU International Technologic Science. vol. 3(2), pp. 29-37.
  • Young, A., (2004). Automatic Acronym Identification and the Creation of an Acronym Database. The Technical Report, The University of Sheffield.
  • Zhou, Z.-H., (2003). Three Perspectives of Data Mining. Artificial Intelligence. vol. 143(1), pp. 139–146.

Yıl 2016, Cilt: 8 Sayı: 3, 19 - 26, 01.12.2016

Özlem Terzi Tahsin Baykal

Öz

Kaynakça

  • Braha, D., Shmilovici, A. (2002). Data mining for improving a cleaning process in the semiconductor industry. IEEE Transactions on Semiconductor Manufacturing. vol.15, 1.
  • Cunningham, S. J., Holmes, G. (1999). Developing innovative applications in agriculture using data mining. Proceedings of Southeast Asia Regional Computer Confederation Conference, Singapore.
  • Dogan, E. (2009). Prediction of sediment concentration using artificial neural Networks. Turkish Chamber of Civil Engineers, vol. 302, pp. 4567-4582.
  • Fayyad, U.M., Uthurusamy, R., (2002). Evolving data mining into solutions for insights. Communications of the ACM. vol. 45(8), pp. 28-31.
  • Hall , M.J., Minns, A.W., Ashrafuzzaman, A.K.M. (2002). The application of data mining techniques for the regionalisation of hydrological variables. Hydrology and Earth System Sciences. vol. 6(4), pp. 685-694.
  • Hall, M., Holmes, G., Frank, E. (1999). Generating Rule Sets from Model Trees. Proceedings of the Twelfth Australian Joint Conference on Artificial Intelligence. pp. 1-12., Sydney, Australia .
  • Heng, S. and Suetsugi, T. (2013). Using Artificial Neural Network to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River Basin, Cambodia. Journal of Water Resource and Protection, vol. 5(2), pp. 111-123.
  • Hoffmann, D., Apostolakis, J. (2003). Crystal Structure Prediction by Data Mining. Journal of Molecular Structure. vol. 647, pp. 17-39.
  • http://www.investopedia.com/ terms/m/mlr.asp.
  • http://homepages.inf.ed.ac.uk/jcavazos/SMART07/ paper_9_9.pdf.
  • Keskin, M. E., Taylan, D., Kucuksille, E. U. ( 2013). Data Mining Process for Modelling Hydrological Time Series. Hydrology Research. vol. 44 (1), pp. 78-88.
  • Keskin, M.E., Terzi, Ö., Küçüksille, E.U., (2009). Data mining process for integrated evaporation model. Journal of Irrigation and Drainage Engineering. vol. 135(1), pp. 39-43.
  • Li, S.T., Shue, L.Y., (2004). Data mining to aid policy making in air pollution management. Expert System and Applications. vol. 27, pp. 331-340.
  • Lin, C.T., Lee, C.S.G. (1995). Neural fuzzy systems. Prentice Hall.
  • Mattison, R. (1996). Data Warehousing: Strategies, Technologies and Techniques Statistical Analysis. SPSS Inc. WhitePapers.
  • Mirbagheri, S. A., Nourani., V., Rajaee, T., Alikhani, A.(2010). Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers.
  • Hydrological Sciences Journal – Journal des Sciences Hydrologiques. vol. 55(7), pp. 1175- 1189.
  • Mishra, S., Dwivedi, V. K., Saravanan, C., Pathak, K. K. (2013). Pattern Discovery in Hydrological Time Series Data Mining during the Monsoon Period of the High Flood Years in Brahmaputra River Basin. International Journal of Computer Applications. vol. 67(6), pp. 7-14.
  • Rupp, B., Wang, J. (2004). Predictive Models For Protein Crystallization. Methods, vol. 34, pp. 390-407.
  • Terzi, Ö., (2011). Monthly River Flow Forecasting by Data Mining Process. KnowledgeOriented Applications in Data Mining, K. Funatsu (Ed.). InTech.
  • Terzi, Ö., Küçüksille, E.U., Ergin, G., İlker, A. (2011). Estimation of Solar Radiation Using Data Mining Process. SDU International Technologic Science. vol. 3(2), pp. 29-37.
  • Young, A., (2004). Automatic Acronym Identification and the Creation of an Acronym Database. The Technical Report, The University of Sheffield.
  • Zhou, Z.-H., (2003). Three Perspectives of Data Mining. Artificial Intelligence. vol. 143(1), pp. 139–146.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İnşaat Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Özlem Terzi

Tahsin Baykal Bu kişi benim

Yayımlanma Tarihi 1 Aralık 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 8 Sayı: 3

Kaynak Göster

IEEE Ö. Terzi ve T. Baykal, “DATA MINING PROCESS FOR RIVER SUSPENDED SEDIMENT ESTIMATION”, UTBD, c. 8, sy. 3, ss. 19–26, 2016.

Dergi isminin Türkçe kısaltması "UTBD" ingilizce kısaltması "IJTS" şeklindedir.

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