상하수도학회지 (Journal of Korean Society of Water and Wastewater)

  • 제37권6호
  • /
  • Pages.363-373
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  • 2023
  • /
  • 1225-7672(pISSN)
  • /
  • 2287-822X(eISSN)
대한상하수도학회 (The Korean Society of Water and Wastewater)
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사회인구통계 및 상수도시설 특성을 고려한 소블록 단위 물 수요예측 연구

Water demand forecasting at the DMA level considering sociodemographic and waterworks characteristics

  • Saemmul Jin (Department of Environmental Engineering, University of Seoul) ;
  • Dooyong Choi (K-water Research Institute) ;
  • Kyoungpil Kim (K-water Research Institute) ;
  • Jayong Koo (Department of Environmental Engineering, University of Seoul)
  • 투고 : 2023.10.17
  • 심사 : 2023.11.20
  • 발행 : 2023.12.15
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초록

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea's SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

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참고문헌

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