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APPLICATIONS OF XGBOOST AND SARIMA FORECASTING MODELS ON WATER CONSUMPTION OF MSU-TCTO: A DATA-DRIVEN APPROACH TO WATER RESOURCE MANAGEMENT
Keywords:
SARIMA model, XGBoost modelDOI:
https://doi.org/10.17654/0972361724032Abstract
In this study, we used the SARIMA and XGBoost models to determine which model is more accurate in predicting the water consumption of MSU-TCTO Main Campus. We utilized a secondary data type of the monthly water consumption (in cubic meters) of MSU-TCTO Main Campus covering January 2015 to December 2022. It is shown that the XGBoost model is the best model for predicting the water consumption of MSU-TCTO Main Campus.
Received: December 1, 2023
Accepted: March 6, 2024
References
R. El-Nwsany, I. Maarouf and W. Abd El-Aal, Water management as a vital factor for a sustainable school, Alexandria Engineering School 50 (2019), 303-313. DOI: 10.1016/j.aej.2018.12.012.
W. Velicer and J. Fava, Time series analysis for psychological research, Research Methods in Psychology, Vol. 2, Handbook of Psychology, 2003, pp. 581-606.
Z. Fang, S. Yang, C. Lv, S. An and W. Wu, Application of a data-driven XGBoost Model for the prediction of COVID-19 in the USA: a time-series study, BMJ Open 12 (2022), e056685. DOI: 10.1136/bmjopen-2021-056685.
X. Nguyen, Identification of Monte-Carlo Photon conversions from simulations in ALICE using XGBoost, Undergraduate Thesis, Ruprecht-Karls-Universität Heidelberg, 2021.
A. Srinivasulu, S. Valli, P. Hussainkhan and P. Anitha, A survey on disease prediction in bid data healthcare using extended convolutional neural network, National Conference on Emerging Trends in Information, Management and Engineering Sciences, 2018, pp. 1-5.
C.-C. Chung, E. C.-Y. Su, J.-H. Chen and Y.-T. Chen, XGBoost-based simple three-item model accurately predicts outcomes of acute ischemic stroke, Diagnostics 13 (2023), 842.
T. Hu and T. Song, XGBoost academic forecasting and analysis modelling, Journal of Physics: Conference Series 1324 (2019), 012091.
DOI: 10.1088/1742.6596/1324/1/1/012091
E. Kalashak, Prediction of water consumption using machine learning: using machine learning techniques to predict hourly water consumption, Master’ Thesis, Østfold University College, 2021.
A. Ibrahim, A. Osman, A. Ahmed, M. F. Chow, Y. F. Huang and A. El-Shafie, Extreme gradient boosting (XGBoost) model to predict the groundwater levels in Selangor, Malaysia, Ain Shams Engineering Journal 12(2) (2021), 1545-1556. DOI: 10.1016/j.asej.2020.11.011.
A. Panarese, G. Settanni, V. Vitti and A. Galiano, Developing and preliminary testing of a machine learning-based platform for sales forecasting using a gradient boosting approach, Appl. Sci. 12(21) (2022), 11054.
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