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.
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
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.