Estimasi Konsentrasi Polutan Air Sungai Dengan Menggunakan Metode Kalman Filter

Nurjanah, Istiazah Laili (2025) Estimasi Konsentrasi Polutan Air Sungai Dengan Menggunakan Metode Kalman Filter. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pencemaran sungai akibat limbah domestik, industri, dan pertanian menyebabkan penurunan kualitas air yang berdampak pada ekosistem dan kesehatan masyarakat. Salah satu tantangan dalam pemantauan kualitas air adalah keterbatasan data akibat kendala teknis dalam pengukuran. Penelitian ini bertujuan untuk mengembangkan model matematis yang merepresentasikan interaksi antara polutan dalam sistem perairan serta menerapkan metode Kalman Filter untuk mengestimasi konsentrasi polutan yang tidak tersedia dalam data pemantauan langsung. Polutan yang dianalisis meliputi amonia (NH4), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO). Data yang digunakan diperoleh dari sistem Online Monitoring (Onlimo) di Sungai Kedunguling, Sidoarjo. Konsentrasi NOx dan NORG yang tidak tersedia dalam statsiun pemantauan akan diestimasi menggunakan metode Kalman Filter. Selain itu, estimasi parameter dilakukan menggunakan metode Kalman Filter untuk meningkatkan akurasi model, di mana parameter α_1, γ, μ, dan ρ diestimasi. Validasi model dilakukan dengan membandingkan hasil simulasi terhadap data observasi menggunakan metrik Root Mean Square Error (RMSE). Hasil menunjukkan bahwa model dapat merepresentasikan dinamika pencemaran air secara kuantitatif dan memiliki tingkat akurasi yang baik.
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River pollution caused by domestic, industrial, and agricultural waste leads to a decline in water quality, affecting both ecosystems and public health. One of the challenges in water quality monitoring is the lack of sufficient data due to technical limitations in measurement. This study aims to develop a mathematical model that represents the interactions between pollutants in aquatic systems and to apply the Kalman Filter method to estimate the concentrations of pollutants that are not available in direct monitoring data. The pollutants analyzed include ammonia (NH4), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO). The data used were obtained from the Online Monitoring System (Onlimo) in Kedunguling River, Sidoarjo. Nitrogen Oxides (NOₓ) and Organic-N (NORG) concentrations, which are not available in the monitoring stations, are estimated using the Kalman Filter method. Additionally, parameter estimation using the Kalman Filter was conducted to improve model accuracy, focusing on estimating parameters α_1, γ, μ, and ρ. Model validation was carried out by comparing simulation results with observational data using the Root Mean Square Error (RMSE) metric. The results show that the model can quantitatively represent water pollution dynamics and achieve a high level of accuracy.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Kalman Filter, Pencemaran Air Sungai, Onlimo, Estimasi Polutan, Kalman Filter, River Pollution, Onlimo, Pollutant Estimation
Subjects:	Q Science > QA Mathematics > QA402.3 Kalman filtering.
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Istiazah Laili Nurjanah
Date Deposited:	04 Aug 2025 01:56
Last Modified:	04 Aug 2025 01:56
URI:	http://repository.its.ac.id/id/eprint/126580

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