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Article

Ensemble Model Development for the Prediction of a Disaster Index in Water Treatment Systems

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Department of Civil and Environmental Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Korea
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G&C Environmental Solution, 16-5, Seongmisan-ro 23-gil, Mapo-gu, Seoul 03979, Korea
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Korea Institute of Civil Engineering and Building Technology, 283, Goyang-daero, Ilsanseo-gu, Goyang-si, Gyeonggi-do 10223, Korea
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Department of Environmental Energy Engineering, University of Suwon, 17, Wau-an-gil, Bongdam-eup, Hwaseong-si, Gyeonggi-do 18323, Korea
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Disaster Prevention Research Division, National Disaster Management Research Institute, 365, Jongga-ro, Jung-gu, Ulsan 44538, Korea
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Bayesian AI Laboratory, BAIES, Fairfax, VA 22030, USA
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Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Dr., Orlando, FL 32816, USA
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Department of Information & Statistics, Chungbuk National University, Chungdae-Ro 1, SeoWon-Gu, Cheongju, Chungbuk 28644, Korea
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Authors to whom correspondence should be addressed.
Water 2020, 12(11), 3195; https://doi.org/10.3390/w12113195
Received: 17 October 2020 / Revised: 10 November 2020 / Accepted: 13 November 2020 / Published: 15 November 2020
(This article belongs to the Section Water Resources Management, Policy and Governance)
The quantitative analysis of the disaster effect on water supply systems can provide useful information for water supply system management. In this study, a total disaster index (TDI) was developed using open-source public data in 419 water treatment plants in Korea with 23 input variables. The TDI quantifies the possible effects or damage caused by three major disasters (typhoons, heavy rain, and earthquakes) on water supply systems. The four components (regional factor, risk factor, urgency factor, and response and recovery factor) were calculated using input variables to determine the disaster index (DI) of each disaster. The weight of the input variables was determined using principal component analysis (PCA), and the weights of the DI of three natural disasters and four components used to calculate the TDI were determined by the analytical hierarchy process (AHP). Specifically, two ensemble machine learning models, random forest (RF) and XGBoost (XGB), were used to develop models to predict the TDI. Both models predicted the TDI with the coefficient of determination and root-mean-square error-observations standard deviation ratio of 0.8435 and 0.3957 for the RF model and 0.8629 and 0.3703 for the XGB model, respectively. The relative importance analysis suggests that the number of input variables can be minimized, which improves the models’ practical applicability. View Full-Text
Keywords: disaster management; ensemble model; machine learning; water supply; water treatment system disaster management; ensemble model; machine learning; water supply; water treatment system
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MDPI and ACS Style

Park, J.; Park, J.-H.; Choi, J.-S.; Joo, J.C.; Park, K.; Yoon, H.C.; Park, C.Y.; Lee, W.H.; Heo, T.-Y. Ensemble Model Development for the Prediction of a Disaster Index in Water Treatment Systems. Water 2020, 12, 3195. https://doi.org/10.3390/w12113195

AMA Style

Park J, Park J-H, Choi J-S, Joo JC, Park K, Yoon HC, Park CY, Lee WH, Heo T-Y. Ensemble Model Development for the Prediction of a Disaster Index in Water Treatment Systems. Water. 2020; 12(11):3195. https://doi.org/10.3390/w12113195

Chicago/Turabian Style

Park, Jungsu; Park, Jae-Hyeoung; Choi, June-Seok; Joo, Jin C.; Park, Kihak; Yoon, Hyeon C.; Park, Cheol Y.; Lee, Woo H.; Heo, Tae-Young. 2020. "Ensemble Model Development for the Prediction of a Disaster Index in Water Treatment Systems" Water 12, no. 11: 3195. https://doi.org/10.3390/w12113195

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