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Modeling the Level of Drinking Water Clarity in Surabaya City Drinking Water Regional Company Using Combined Estimation of Multivariable Fourier Series and Kernel

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  • Andi Tenri Ampa

    (Department of Statistic, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
    Department of Statistic, Halu Oleo University, Kendari 93132, Indonesia)

  • I Nyoman Budiantara

    (Department of Statistic, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia)

  • Ismaini Zain

    (Department of Statistic, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia)

Abstract

The purpose of this study is to propose an appropriate model to predict chemical composition during water purification at the Regional Water Company (PDAM) Surabaya, in order to achieve proper drinking water standards. Drinking water treatment is very expensive, so the model serves as a basis for determining the composition of chemicals used in the water purification process at PDAM Surabaya. This study examines a model of the relationship between the level of clarity of drinking water and the composition of the chemicals used. The government can obtain important benefits from the forecasting model to formulate policies for the company. One of the objectives of developing the estimation method involved in this research is to efficiently determine the exact chemical composition resulting from the water purification process, which will inform the financing and control of water quality. We used a multivariable linear approach for some parametric components, a multivariable Fourier Series approach for some nonparametric components, and a multivariable Kernel approach for semiparametric regression. Using the penalized least square (PLS) approach, a mixed estimator of the Fourier and Kernel Series was obtained with semiparametric regression. The smoothing parameters were selected using a common cross-validation technique (GCV). The performance of this technique was evaluated using the Gaussian Kernel and Fourier Series with data trends in the drinking water clarity level obtained from PDAM Surabaya. The findings showed that this technique performed well, so we recommend that the government conduct an in-depth analysis to determine correct chemical composition so that the cost of water treatment can be minimized.

Suggested Citation

  • Andi Tenri Ampa & I Nyoman Budiantara & Ismaini Zain, 2022. "Modeling the Level of Drinking Water Clarity in Surabaya City Drinking Water Regional Company Using Combined Estimation of Multivariable Fourier Series and Kernel," Sustainability, MDPI, vol. 14(20), pages 1-12, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13663-:d:949670
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    References listed on IDEAS

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    1. Okumura, Hidenori & Naito, Kanta, 2006. "Non-parametric kernel regression for multinomial data," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 2009-2022, October.
    2. Smith, Michael & Kohn, Robert & Mathur, Sharat K., 2000. "Bayesian Semiparametric Regression: An Exposition and Application to Print Advertising Data," Journal of Business Research, Elsevier, vol. 49(3), pages 229-244, September.
    3. Manzan, Sebastiano & Zerom, Dawit, 2005. "Kernel estimation of a partially linear additive model," Statistics & Probability Letters, Elsevier, vol. 72(4), pages 313-322, May.
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