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Pemodelan Indikator Pencemar Biological Oxygen Demand di Kali Surabaya Menggunakan Pendekatan Spatial-Temporal Weighted Regression

Authors: Choiruddin, Achmad;

Pemodelan Indikator Pencemar Biological Oxygen Demand di Kali Surabaya Menggunakan Pendekatan Spatial-Temporal Weighted Regression

Abstract

Kali Surabaya merupakan sungai di Surabaya yang memiliki tingkat pencemaran sungai yang cukup memprihatinkan padahal sekitar 96 persen air baku Perusahaan Daerah Air Minum PDAM Kota Surabaya dipasok dari Kali Surabaya. Untuk mengetahui tingkat pencemaran air perlu untuk mengetahui faktor-faktor yang menyebabkan perubahan kadar BOD. Beberapa penelitian mengenai pencemaran air Kali Surabaya selama ini hanya mampu mengakomodasi sebatas pada efek heterogen spasial padahal BLH Surabaya mulai merasakan adanya efek spasial-temporal pada kasus BOD Kali Surabaya. Penelitian ini menggunakan metode Spatial Temporal Weighted Regression STWR karena metode ini mampu mengakomodasi heterogenitas spasial-temporal. Hasil penelitian menunjukkan bahwa dengan metode STWR memberikan kinerja yang lebih baik daripada metode GWR dan regresi global. Selain itu diketahui bahwa efek heterogen temporal lebih mendominasi daripada efek spasial karena koefisien parameter dan jumlah parameter antar waktu lebih beragam daripada koefisien parameter dan jumlah parameter antar lokasi. Faktor-faktor yang berpengaruh terhadap BOD Kali Surabaya adalah Flourida Fosfat Nitrat NH3 dan Nitrit.

Kali Surabaya is a river in Surabaya that has a high level of water pollution whereas approximately 96 percent of standard water Regional Water Company PDAM Surabaya was supplied by Kali Surabaya. To determine the level of water pollution it is needed to know the factors that cause changes in levels of BOD. Some research on water pollution in Surabaya so far are only able to accommodate the effect of spatial heterogeneity while BLH Surabaya began to feel the effects of spatial-temporal heterogeneity in the case of BOD levels in Kali Surabaya. This study uses Spatial-Temporal Weighted Regression STWR because this method is able to accommodate the spatiotemporal heterogeneity. The results showed that the method STWR provide better performance than GWR and global regression. In addition, it is known that the temporal effects are more dominant than the spatial effects since the parameters coefficients and the number of parameters over time is more varied than the parameters coefficients and the number of parameters among the locations. Factors affecting the level of BOD in Kali Surabaya are fluoride phosphate nitrate NH3 and Nitrite.

Country
France
Related Organizations
Keywords

[SDE.BE] Environmental Sciences/Biodiversity and Ecology, Spatial Temporal Weighted Regression, [STAT.AP] Statistics [stat]/Applications [stat.AP], Biological Oxygen Demand, Spatial Temporal Heterogeneity

13 references, page 1 of 2

3 Semua X2 , X3 , X5 - X2 , X5 Semua X1 4 Semua X2 , XX35, X4 , X1 X2 , X5 Semua X1 5 Semua X2 , XX35, X4 , X1 X2 , X5 Semua X1 6 Semua X2 , XX35, X4 , XX13, X2 , X5 Semua X1 Ket : (-)= tidak ada variabel prediktor yang signifikan, X1=Flourida, X2=Fosfat, X3=Nitrit, X4=NH3, X5=Nitrat Pada pengamatan Maret 2010, semua variabel prediktor berpengaruh signifikan pada perubahan kandungan BOD pada semua lokasi pengamatan, baik di Kali Surabaya Jembatan Kedurus (1), Kali Surabaya Jembatan Wonokromo (2), Kali Mas Jembatan Ngagel (3), Kali Mas Jembatan Keputran Selatan (4), Kali Mas Jembatan Kebon Rojo (5), dan Kali Jeblokan Jembatan Jalan Petojo (6).

[1] Kusumawardani, D. (2010). Valuasi Ekonomi Air Bersih di Surabaya (Studi Kasus Pada Air PDAM). Yogyakarta: Lembaga Penelitian dan Pengabdian Kepada Masyarakat UGM.

[2] BLH-Surabaya. (2011). Kualitas Air Surabaya Mengalami Penurunan. Retrieved January 30, 2013, from http://www.lh.blhsby.go.id

[3] Purwatiningsih, S. (2005). Kajian Kualitas Kali Surabaya Ditinjau Dari Aspek Lingkungan, Peraturan Perundangan, dan Kelembagaan. Surabaya: Program Sarjana Institut Teknologi Sepuluh Nopember.

[4] Koesnariyanto, R. (2012). Pemodelan Indikator Pencemaran Air Secara Kimia (BOD) Dengan Geographically Weighted Regression. Surabaya: Program Magister Fakultas Kesehatan Masyarakat Universitas Airlangga.

[5] Lumaela, A. K. (2012). Pemodelan Chemical Oxygen Demand Sungai di Surabaya Dengan Metode Mixed Geographically Weighted Regression. Surabaya: Program Sarjana Institut Teknologi Sepuluh Nopember.

[6] Sastrawijaya, A. T. (2000). Pencemaran Lingkungan. Jakarta: Rineka Cipta.

[7] Huang, B., Wu, B., dan Barry, M. (2010). Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in Houses Prices. International Journal of Geographical Information Science , 383-401.

[8] Yu, P.H., dan Lay, J.G. (2011). Exploring Nonstationarity of Local Mechanism of Crime events with Spatial-temporal Weighted Regression. 7-12.

[9] Draper, N., dan Smith, H. (1992). Analisis Regresi Terapan. Jakarta: PT Gramedia Pustaka Utama.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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