Abstract
Two clustering techniques of water quality for canals in Bangkok were compared: K-means and Fuzzy c-means. The result illustrated that K-means has a better performance. As a result, K-means cluster was used to classify 24 canals of 344 records of surface water quality within Bangkok; the capital city of Thailand. The data was obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2005-2008. Water samples were collected and analyzed on 13 different parameters: temperature, pH value (pH), hydrogen sulfide (H2S), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), substance solid (SS), total kjeldahl nitrogen (TKN), ammonia nitrogen (NH3N), nitrite nitrogen (NO2N), nitrate nitrogen (NO3N), total phosphorous (T-P) and total coliform. The data were analyzed and clustered. The results of cluster analysis divided the canals into five clusters. The information from clustering could enhance the understanding of surface water usage in the area. Additionally, it can provide the useful information for better planning and watershed management of canals in Bangkok.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bengraine, K., Marhaba, T.F.: Using principle component analysis to monitor spatial and temporal changes in water quality. Journal of Hazardous Materials 100(1-3), 179–195 (2003)
Guler, C., Thyne, G.D.: Hydrologic and geologic factors controlling surface and groundwater chemistry in Indian wells-Owens Valley area, southeastern California. Journal of Hydrology 285(1-4), 177–198 (2004)
MacQueen, J.B., Foster, I., Kesselman, C.: Some Methods for classification and Analysis of Multivariate Observations: Procee C.: The Grid: Blueprint for a New Computing Infrastructure. In: Proc. Fifth Berkeley Symp. on Math. Statist. and Prob., vol. 1, pp. 281–297. Univ. of. Calif. Press, Berkeley (1967)
Dogan, E., Sengorur, B., Koklu, R.: Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. Journal of Environmental Management (2008)
Areerachakul, S., Sanguansintukul, S.: Water Classification Using Neural Network: A Case Study of Canals in Bangokok. In: The 4th International Conference for Internet Technology and Secured Transactions (ICITST 2009), Thailand. IEEE Press, United Kingdom (2009)
Anazawa, K., Ohmori, H.: The hydrochemistry of surface waters in Andesitic Volcanic area, Norikura Volcano, central Japan. Chemosphere 59(5), 605–615 (2005)
Astel, A., Tsakovski, S., Simeonov, V., Reisenhofer, E., Piselli, S., Barbieri, P.: Multivariate classification and modeling in surface water pollution estimation. Analytical and Bioanalytical Chemistry 390(5), 1283–1292 (2008)
Binbib, H., Fang, T., Guo, D.: Quality assessment and uncertainty handling in spatial data mining. In: Proc. 12th Conference on Geoinformations, Sweden (2004)
Helena, B., Pardo, R., Vega, M., Barrado, E., Fernández, J.M., Fernández, L.: Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Research 34(3), 807–816 (2000)
Kanungo, T., Mount, D., Netanyahu, N., Piatko, D.C., Silverman, R.: An Efficient K-means Clustering Algorithm Analysis and Implementation. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(7) (July 2002)
Laaksoharju, M., Gurban, I., Skarman, C., Skarman, E.: Multivariate mixing and mass balance (M3) calculations, a new tool for decoding hydrogeochemical information. Applied Geochemistry 14(7), 861–871 (1999)
Liu, C.W., Lin, K.H., Kuo, Y.M.: Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. The Science of the Total Environment 313(1-3), 77–89 (2003)
Halkidi, M., Vazirgiannis, M.: Cluster validity assessment: finding the optimal partitioning of a dataset. In: Proc. ICDM Conference, San Jose, Californaia, USA, pp. 187–194 (1996)
Simeonov, V., Stratis, J.A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., Sofoniou, M., Kouimtzis, T.: Assessment of the surface water quality in Northern Greece. Water Research 37(17), 4119–4124 (2003)
Simeonova, P., Simeonov, V., Andreev, G.: Water quality study of the Struma River Basin, Bulgaria. Central European Journal of Chemistry 2, 121–136 (2003)
Singh, K.P., Malik, A., Sinha, S.: Water quality assessment and apportionment of pollution sources of Gomti River (India) using multivariate statistical techniques: a case study. Analytica Chimica Acta 538(1-2), 355–374 (2005)
Vega, M., Pardo, R., Barrado, E., Deban, L.: Assessment of seasonal and polluting effects on the quality of river water byex ploratory data analysis. Water Research 32(12), 3581–3592 (1998)
Bezdek, J.C.: Fuzzy partition and relations: an axiomatic basic for clustering. Fuzzy Sets and Systems 1, 111–127 (1978)
Liou, S., Lo, S., Hu, C.: Application of two-stage fuzzy set theory to river quality evaluation in Taiwan. Water Research 37, 1406–1416 (2003)
Ministry of Natural Resource and Environment, http://www.mnre.go.th
Department of Drainage and Sewerage Bangkok Metropolitan Administration, http://dds.bangkok.go.th/wqm/thai/home.html
Department of Pollution Control Bangkok, http://www.pcd.go.th
Ministry of Public Health, http://eng.moph.go.th
Luke, B.: K-Means Clustering, http://fconyx.ncifcrf.gov/~lukeb/kmeans.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Areerachakul, S., Sanguansintukul, S. (2010). Clustering Analysis of Water Quality for Canals in Bangkok, Thailand. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12179-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-12179-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12178-4
Online ISBN: 978-3-642-12179-1
eBook Packages: Computer ScienceComputer Science (R0)