WSI: A New Early Warning Water Survival Index for the Domestic Water Demand

Dong-Her Shih, Ching-Hsien Liao, Ting-Wei Wu (), Huan-Shuo Chang and Ming-Hung Shih
Additional contact information
Dong-Her Shih: Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Ching-Hsien Liao: Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Ting-Wei Wu: Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Huan-Shuo Chang: Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Ming-Hung Shih: Department of Electrical and Computer Engineering, Iowa State University, 2520 Osborn Drive, Ames, IA 50011, USA

Mathematics, 2022, vol. 10, issue 23, 1-19

Abstract: A reservoir is an integrated water resource management infrastructure that can be used for water storage, flood control, power generation, and recreational activities. Predicting reservoir levels is critical for water supply management and can influence operations and intervention strategies. Currently, the water supply monitoring index is used to warn the water level of most reservoirs. However, there is no precise calculation method for the current water supply monitoring index to warn about the adequacy of the domestic water demand. Therefore, taking Feitsui Reservoir as an example, this study proposes a new early warning water survival index (WSI) to warn users whether there is a shortage of domestic water demand in the future. The calculation of WSI was divided into two stages. In the first stage, the daily rainfall, daily inflow, daily outflow, and daily water level of the Feitsui Reservoir were used as input variables to predict the water level of the Feitsui Reservoir by the machine learning method. In the second stage, the interpolation method was used to calculate the daily domestic water demand in Greater Taipei. Combined with the water level prediction results of the Feitsui Reservoir in the first stage, the remaining estimated days of domestic water supply from the Feitsui Reservoir to Greater Taipei City were calculated. Then, the difference between the estimated remaining days of domestic water demand and the moving average was converted by the bias ratio to obtain a new WSI. WSI can be divided into short-term bias ratios and long-term bias ratios. In this study, the degree of the bias ratio of WSI was given in three colors, namely, condition blue, condition green, and condition red, to provide users with a warning of the shortage of domestic water in the future. The research results showed that compared with the existing water supply monitoring index, the new WSI proposed in this study can faithfully present the warning of the lack of domestic water demand in the future.

Keywords: reservoir water level prediction; water supply monitoring index; machine learning; water survival index; bias ratio (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/23/4478/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/23/4478/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:23:p:4478-:d:985769

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().