AN INTEGRATED APPROACH FOR THE PREDICTION OF WATER QUALITY INDEX BASED ON LAND USE ATTRIBUTES USING DATA GENERATION METHOD AND BACK PROPAGATION NETWORK ALGORITHM

Authors

  • Faris Gorashi Kulliyyah of Architecture and Environmental Design INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA
  • Alias Abdullah Kulliyyah of Architecture and Environmental Design INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DOI:

https://doi.org/10.21837/pm.v7i1.73

Keywords:

Water Quality Index, ANN, Gombak River, Back Propagation, Land-Use Data Generation

Abstract

Malaysian experts have warned that several major cities, including the capital Kuala Lumpur, could face serious water shortages due to over-pollution of the country's rivers by problems brought about by over-development. As 97 per cent of Malaysia's water supply is sourced from surface water, the main aim of this study was to identify a relationship between water quality and land use attributes. The study which was conducted on Gombak River and its watershed in Malaysia, introduced data generation method for the prediction and forecast of LU/LC data within the watershed. The method used exponential model equation, Lagrange model equation third & fourth degree polynomial fit; saturation growth-rate model in order to generate the required data: and artificial neural network's back propagation network algorithm. The study also introduces the LA-WQI model. This model was developed by associating the appropriate loading factors to a set of sub-indices. The findings revealed that as the activities increased throughout the watershed, the values of WQI quality decreased accordingly. The accuracy of prediction of the proposed LA-WQI ranged from 94.3% to 99.3% between Actual DOE-WQI and LA-WQI for station 18 in Gombak River. The results of predicted WQI obtained using LA-WQI, showed a continuous decrease of water quality. Despite the high accuracy attained by the application of LA-WQI model on Gombak River; it has not yet been tested on other rivers. It is recommended that future studies should be able to further test the current model on a regional scale.

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References

Alias Abdullah, Kazuhisa Iki, and Mitsuo Morozumi, 1994. A study to evaluate change of zoning in GIS operation due to the diversification of the AHP judgment, Proceeding of the Sixteenth Symposium on Computer Technology' of Information, System and Applications, AIJ, S-5-6, pp.349-354.

Alias Abdullah, Kazuhisa Iki, and Mitsuo Morozumi, 1994. An integrated approach of AHP and GIS application to analyze and develop recreational zoning, Journal of architecture planning and Environmental Engineering, AIJ, No. 463, pp. 213-222.

Bishop, J. E., 1973. Limnology> of a Small Malayan River, Sungai Gombak, The Hague, pp. 485.

Carr, G.M. and Chambers, P.A., 1998. Spatial and temporal patterns of nutrients and algal abundance in Alberta rivers. Report prepared for the Prairie Provinces Water Board, Regina, SK.

Chandra Sekhar, M. and Anand Raj, P., 1995. Land Use Water Quality Modeling, Journal Of Water Science Technology, Vol. 31, No. 8, IWA Publishing, pp.383-386.

Correll, D.L., Jordan, T.E., and Weller, D.E., 2001. Effects of precipitation, air temperature, and land use on organic carbon discharges from Rhode River watersheds. Water, Air, and Soil Pollution, 128, pp. 139-159.

Diane M. L., David P. A., 2004. Use of Artificial Neural Network Models to Predict Indicator Organisms Concentrations in an Urban Watershed, AGU, 85(17), Joint Assembly Suppl., Abstract H53A-06 1330h POSTER

Engel, B. R., Srinivasan, R., Arnold,J., Rewerts,C., and Brown, S. J., 1993. Nonpoint Source (NPS) Pollution Modelling Using Models Integrated with Geographic Information Systems (GIS). Water Science and Technolog>, Vol. 28, No. 3-5, pp. 685-690.

Evans, B. M. and Miller, D. A., 1988. Modeling Nonpoint Pollution at the Watershed Level with the Aid of a Geographic Information System. In: Nonpoint Pollution: 1988 - Policy, Economy, Management, and Appropriate Technology. American Water Resources Association, pp. 283-290.

Faris, F.G. and Alias Abdullah, 2008. A Study on the Water Quality of Gombak River Using Artificial Neural Network, Al-Rissala Journal, International Islamic University Malaysia 2008 (In press).

Faris, F.G. and Alias Abdullah, 2008. Prediction of Water Quality Index Using Back Propagation Network Algorithm. Case Study: Gombak River, Journal of Engineering Science and Technology 2008 (In press).

Goodchild, M. F., Parks, B. O., and Steyaert, L. T., (eds.), 1993. Geographic Information Systems and Environmental Modeling, Oxford University Press, New York, pp. 231-237.

Hafizan Juahir, Sharifuddin M. Zain, Zainal Ahmad, Nazari M. Jaafar, 2004. An Application of Second Order Neural Network Back Propagation Method in Modeling River Discharge. In Water Environmental Planning: Towards Integrated Planning and Management of Water Resources for Environmental Risks, Alias Abdullah, Norio Okada, and Mohd Kamil Yusoff, eds., International Islamic University Malaysia, pp. 307 - 324.

Hubert-Moy, L., Cotonnec, A., Le Du, L., Chardin, A., and Perez, P., (eds.), 2001. A Comparison of Classification Procedures of Remote Sensed Data Applied on Different Landscape Units, Remote Sensing of the Environment, Elsevier, Vol. 75, no. 2, pp. 174-187.

Junaida Ariffm, Aminuddin Abdul Ghani, Nor Azazi Zakaria, and Ahmad Shukri Yahya, 2004. Sediment Prediction Using ANN and Regression Approach, 1st International Conference on Managing Rivers in the 21st Centuiy: Issues & Challenges, Malaysia pp. 168-174.

Kadri, Yurekli, Ahmet Kurunc, and Huseyin Simsek, 2004. Prediction of daily Stream Flow Based on Stochastic Approaches, Journal of Spatial Hydrology, Vol. 4 No. 2.

Kamarul Ismail, and Ruslan Rainis, 2004. Modeling River Water Quality Index Using Artificial Neural Networks and Geographical Information System, In Water Environmental Planning: Towards Integrated Planning and

Management of Water Resources for Environmental Risks, Alias Abdullah, Norio Okada, and Mohd Kamil Yusoff, eds., International Islamic University Malaysia.

Lai, F.S., 1983. Biochemical Oxygen Demand Concentration of Two River Basins of Selangor, Perlanika. Vol. 6(3), pp. 32-43.

Loke, E., Wamaars E. A., Jacobsen P., Nelen F. and Ceu Almeida M., 1997. Artificial Neural Networks as a tool in Urban Storm Drainage, Journal of Water Science and Technology, Vol. 36, Issues 8-9, pp. 101-109.

Noorazuan M. H, Ruslan Rainis, Hafizan Huahir, Sharifuddin, M. Zain, and Nazari Jaafar, 2003. GIS Application in Evaluating Land use - Land Cover Change and its Impact on the Hydrological Regime in Langat River Basin, Malaysia, Map Asia Conference 2003, GISdevelopment.net

Picton, P. D., 1994. Introduction to neural network, the Macmillan press ltd, London.

Rounds, S.A., 2002. Development of a Neural Network Model for Dissolved Oxygen in the Tualatin River, Oregon, Proceeding of the second Federal Interagency Hydrologic Modeling Conference, Las Vegas, Nevada, July 29 - August 1, 2002 Subcommittee on Hydrology of the interagency Advisory Committee on Water Resources.

Ruslan Rainis, 2003. Application of GIS and Landscape Metrics in Monitoring Urban Land Use Change, “Urban Ecosystem Studies in Malaysia. A Study of Changeâ€, Noorazuan Md Hashim & Ruslan Rainis, eds., Universal Publishers, pp 267-278

Sekhar M. Chandra, and Sreenivasulu, 2003. Modeling Nutrients Contributed by Overland Flow From the Krishna River Basin, Diffuse Pollution Conference. Dublin, Water Resources Management, 1A, pp. 20-23.

Smith, A. J., Goetz, S.D., Prince, R. and Wright, B., 2003. Estimation of subpixel impervious surface area using a decision tree approach, Remote sensing of the environment, (in press).

Tong, S. T. Y. and Chen, W., 2002. Modeling the relationship between land use and surface water quality. Journal of Environmental Management, 66: pp. 377-393.

Wang, X., 2001. Integrating water-Quality management and Landuse Planning In a Watershed Context. Journal of environmental management, 61, pp. 25-36.

Watts, R.L., 1966. New Federations: Experiments in the Commonwealth. Oxford: Clarendon Press.

Wenwei Ren, Yang Zhong, Meligrana, J., Anderson, B., Watt, W.E., Jiakuan Chen, and Hok-Lin Leung, 2003. Urbanization, Land use, and Water Quality in Shanghai, Environment International, 29, Elsevier, pp. 649-659

Yusoff, M.K., Heng, S.S., Majid, N.M., Mokhtaruddin, A.M., Hanum, I.F., Alias, M.A, Kobayashi, S., 2001. Effects of different land use patterns on the stream water quality in Pasoh, Negeri Sembilan, Malaysia. In Rehabilitation of Degraded Tropical Forest Ecosystems: Workshop Proceedings, Kobayashi, S., Turnbull, J.W., Toma, T. Mori, T., Majid, N.M.N.A. eds., 2-4 November 1999, Cl FOR, Bogor, Indonesia, pp. 87-98.

Zou, R., Lung, W.S., and Guo, H., 2002. Neural Network Embedded Monte Carlo Approach for Water Quality Modeling Under Input Information Uncertainty. Journal of Computing in Civil Engineering, ASCE, 16 (2), pp. 135- 142.

Internet:

Cormac Technologies Inc. 1999. Manual, NeuNet Pro, Revision 2.2. Cormac Technologies Inc. <http://www.cormactech.com/Neunet>

Dixon, B., 2004. Applicability of Neuro-Fuzzy Techniques In Predicting Ground Water Vulnerability: A GIS-based sensitivity analysis. Journal of Hydrology, published by Elsevier B. V. doi:10.1016/jhydrol.2004.11.010. Retrieved online on 22/04/2005. <http://www.sciencedirect.com>

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Published

2009-11-30

How to Cite

Gorashi, F., & Abdullah, A. (2009). AN INTEGRATED APPROACH FOR THE PREDICTION OF WATER QUALITY INDEX BASED ON LAND USE ATTRIBUTES USING DATA GENERATION METHOD AND BACK PROPAGATION NETWORK ALGORITHM. PLANNING MALAYSIA, 7(1). https://doi.org/10.21837/pm.v7i1.73

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