DOI: https://doi.org/10.14710/presipitasi.v20i3.581-589

Water Demand Modeling using Machine Learning Method in Bandung City, Indonesia

*Evi Afiatun scopus  -  Universitas Pasundan, Indonesia
Yonik Meilawati Yustiani orcid scopus  -  Universitas Pasundan, Indonesia
Dennis Anugerah Tanra  -  Universitas Pasundan, Indonesia

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Abstract

This research was conducted at Bandung City with the aim of building a model using machine learning methods so that it can estimated clean water demands in Bandung City, as well as knowing the external factors that are considered to affect the model. Machine learning is a part of Artificial Intelligence (AI) discipline. The modeling is carried out using independent variables in the form of climate parameters which are rainfall, rainy days, and humidity, as well as the dependent variable in the form of drinking water needs which are represented by raw water. Data collection is done through secondary data. The model was built by using the TPOT module, and produces the AdaBoost.R2 algorithm as the most optimal model, by using the model algorithm, the best sub-model is produced with the most influential external factors, namely rainy days and humidity which has an MAE of 326,077.70 and a MAPE of 4.75%. This model is compared with the ARIMA model which has an MAE of 330,672.088 and an MAPE of 5.07%.

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Keywords: Bandung city; artificial intelligence; machine learning; climate parameters; raw water
Funding: Faculty of Engineering of Universitas Pasundan

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Section: Original Research Article
Language : EN
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  1. Afiatun, E., Notodarmojo, S., Effendi, A., & Sidarto, K. 2018. Cost Minimization of Raw Water Source by Integrated Water Supply Systems (A Case Study for Bandung, Indonesia). International Journal of GEOMATE, 14(46), 32-39
  2. Afiatun, E. , Pradiko, H., & Fabian, E. 2019. Turbidity Reduction for the Development of Pilot Scale Electrocoagulation Devices, International Journal of GEOMATE, 16(56), 123 – 128
  3. Andani, I. G. 2012. Peningkatan Penyediaan Air Bersih Perpipaan Kota Bandung dengan Pendekatan Pemodelan Dinamika Sistem. Jurnal Perencanaan Wilayah dan Kota A SAPPK V1N1, 69 - 78
  4. Antunes, A., Andrade-Campos, A., & Sardinha-Lourenço, A. a. 2018. Short-term Water Demand Forecasting Using Machine Learning Techniques. Journal of Hydroinformatics, 1343–1366
  5. Bakker, M., Vreeburg, J., van Schagen, K., & Rietveld, L. 2013. A Fully Adaptive Forecasting Model for Short-term Drinking Water Demand. Environmental Modelling & Software, 141-151
  6. Bishop, C. M. 2006. Pattern Recognition and Machine Learning. India: Springer
  7. Brownlee, J. 2020. Data Preparation for Machine Learning. Victoria: Machine Learning Mastery
  8. Drucker, H. 1997. Improving Regressors Using Boosting Techniques. Retrieved from ResearchGate: https://www.researchgate.net/publication/2424244_Improving_Regressors_Using_Boosting_Techniques
  9. Duerr, I., Merrill, H., Wang, C., Bai, R., Boyer, M., Dukes, M., & Blinzyuk, N. 2018. Forecasting Urban Household Water Demand with Statistical and Machine Learning Methods Using Large Space-time Data: A Comparative study. Environmental Modelling & Software, 29-38
  10. Foster, T., Mieno, T & Brozović. 2020. Measurement Errors and Their Implications for Agricultural Water Management Policy. Water Resources Research, 56(11): 1-19
  11. Haque, M. M., de Souze, A., & Rahman, A. 2017. Water Demand Modelling Using Independent Component. Water Resources Management, 299-312
  12. Lewis, C. D. 1982. International and Business Forecasting Methods. London: Butterworths
  13. Putri, R.N., Usman, M., Warsono, Widiarti & Virginia, E. 2021. Modeling Autoregressive Integrated Moving Average (ARIMA) and Forecasting of PT Unilever Indonesia Tbk Share Prices During the COVID-19 Pandemic Period. Journal of Physics: Conference Series, 1751: 012027
  14. Siami-Namini, S., Tavakoli, N., & Namin, A.S. 2017. A Comparison of ARIMA and LSTM in Forecasting Time Series. 2018 17th IEEE International Conference on Machine Learning and Applications, 1394-1401

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