Estimation of Surabaya River Water Quality Using Kalman Filter Algorithm

Ali Masduqi, Erna Apriliani


According to previous studies on Surabaya River water, quality of this river water was very bad or on polluted condition. This conclusion was based on monitoring data of water quality at several monitoring points. Because of river water quality is a fluctuative condition, many monitoring data are needed. While, monitoring of Surabaya River water quality was done routinely at nine monitoring points. Amount of the monitoring point is not enough when data will be used for determining water quality condition. For this reason, it is need to develop a method to estimate overall river water quality based on a little amount of data. One of estimation method is Kalman filter, an algorithm that combines a model and a measurement. The experiment of Kalman filter algorithm was conducted. The results were accurate and closely with a measurement. Based on the results, application of Kalman filter algorithm will help to predict water quality for the future and to estimate overall water quality along of river, although amount of measurement data is a little.


Surabaya River; Estimation; Prediction; Kalman Filter

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----------, “Surabaya River Pollution Control Action Plan Study”, Binnie & Partners (overseas) Ltd. 1999.

-----------, “Studi Kualitas Lingkungan Hidup di Jawa Timur”, Lembaga Penelitian ITS – BAPEDAL Provinsi Jawa Timur. 2000.

-----------, “Penelitian: Menuju Air Siap Minum”, PDAM Surabaya dan Jurusan Teknik Lingkungan ITS. 2000

-----------, “Pemetaan Industri Potensi Cemar di Jawa Timur”, Dinas Perindustrian dan Perdagangan Provinsi Jawa Timur - Jurusan Teknik Lingkungan ITS. 2003.

----------, “Water Quality and Resource Protection Strategy

Policy Review”, Volume I, Indonesia East Java Proposed East Java Regional Sector Development and Reform Program

(EJRSDRP), The World Bank - PT Waseco Tirta. 2004.

Siouris, G.M., An Engineering Approach to Optimal Control and Estimation Theory, John Wiley & Sons, Inc. New York. 1996.

Drécourt, JP., “Kalman filtering in hydrological modeling”, DAIHM Technical Report 2003-1, DHI Water & Environment, Agern All 11, 2970 Hørsholm, Denmark. 2003.

Moradkhani, H., Sorooshian, S., Gupta, H.V., dan Houser, P.R., “Dual state–parameter estimation of hydrological models using ensemble Kalman filter”, Advances in Water Resources 28, p. 135–147. 2005.

Schnoor, J.L., Environmental Modeling, Fate and Transport of Pollutants in Water, Air, and Soil, John Wiley & Sons, Inc. New York. 1996.



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