Browse > Article
http://dx.doi.org/10.3741/JKWRA.2016.49.5.399

Statistical analysis of hazen-williams C and influencing factors in multi-regional water supply system  

Kim, Bumjun (Korea Infrastructure Safety and Technology Corporation)
Kim, Gilho (Korea Institute of Civil Engineering and Building Technology)
Kim, Hung soo (Dept. of Civil Engineering, Inha University)
Publication Information
Journal of Korea Water Resources Association / v.49, no.5, 2016 , pp. 399-410 More about this Journal
Abstract
In case of the application of Hazen-Williams C for design, operation or maintenance of water supply system, field situations always should be reflected on the factors. In this study, the relationships between C factors and influencing factors are analyzed using statistical techniques with 174 measured C factor data collected in periodic inspection for safety diagnosis in multi-regional water supply systems. To analyze their relationships, cross analysis, one-way ANOVA, correlation analysis were conducted. Analysis results showed that C factors had high correlations with both of elapsed year and pipe diameter and were relatively highly affected by coating material among influencing factors with the categorical type. On the other hand, elapsed year, pipe diameter and water type were meaningful influencing factors according to the results of multiple regression analysis. The Cluster analysis revealed that C factors had a tendency of being fundamentally classified on the basis of the elapsed year of about 20 years and the pipe diameter of 1500mm. Although C factors were generally greatly affected by elapsed year, size of pipe diameter relatively had an large influence on values of them in case of large diameter pipes. Lastly, It can be suggested that C factor estimation formulas using multiple regression analysis and clustering analysis in this study, can be applied as decision standards of C factor in multi-regional water supply systems.
Keywords
clustering analysis; Hazen-Williams C; influencing factor; multi-regional water supply system; multiple regression analysis; statistics techniques;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Chung, W. S., Lee, H. D., Kim, I. T., Yu, M. J. (2003). Correlation Analysis of Environmental Factor for Drinking Water Pipe Deterioration, Jouranl of the Korean Society of Water and Wastewater, Vol. 17, No. 2, pp. 242-249.
2 Jeon, C. H. (2012). Data Mining Technique and Application, Hannarae Academy, pp 64.
3 Kim, J. K., Park, J. H., Park, H. J., Lee, J. J., Jeon, H. S, Hwang, J. S. (2009). Statistics for Engineer, Free Academy, pp. 247.
4 Kim, J. H., Kim, Z. W., Lee, H. D., Kim, S. H. (1996). Development of Rehabilitation and Management Techniques for Old Water Distribution System, Journal of Korea Water Resources Association, Vol. 29, No. 3, pp. 197-205.
5 Lee, C. H., Ahn, J. H., Lee, J. H., Kim, T. W. (2009). Prediction of Scour Depth Using Incorporation of Cluster Analysis into Artificial Neural Networks, Journal of Korean Society of Civil Engineers, Vol. 29, No. 2B, pp. 111-120.
6 Lee, H. D., K-water (1995). Developement of Decision-Support System for Water Pipeline Rehabilitations.
7 Park, S. W., Im, K. C., Choi, C. L., Kim, K. L. (2009). Hierarchial Clustering Analysis of Water Main Leak Location Data, Journal of Korea Water Resources Association Vol. 42, No. 3, pp. 177-190.   DOI
8 Son, K. I. (1996). Predicting Flow Resistance Coefficients in Water Supply Mains, Journal of Korea Water Resources Association Vol. 29, No. 4, pp. 223-231.
9 Walski, T. M., Sharp, W. W., Shieldsm, F. D. (1988). Prediction Internal Roughness in Water Mains, Miscellaneous Paper EL-88-2, US Army Engineering Waterways Experiment Station.
10 Won, T. Y., Jung, S. W. (2013). SPSS PASW Statistics 18.0 Statistical Research Analysis, Hannarae Academy, pp. 339.