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농업기반시설물 양·배수장의 성능저하 요인분석 및 성능평가 모델 개발

Development of Evaluation Model of Pumping and Drainage Station Using Performance Degradation Factors

  • Lee, Jonghyuk (Department of Rural Systems Engineering, Seoul National University) ;
  • Lee, Sangik (Department of Rural Systems Engineering, Seoul National University) ;
  • Jeong, Youngjoon (Department of Rural Systems Engineering, Seoul National University) ;
  • Lee, Jemyung (Division of Environmental Science and Technology, Kyoto University) ;
  • Yoon, Seongsoo (Department of Agricultural and Rural Engineering, Chungbuk National University) ;
  • Park, Jinseon (Department of Agricultural and Rural Engineering, Chungbuk National University) ;
  • Lee, Byeongjoon (Department of Agricultural and Rural Engineering, Chungbuk National University) ;
  • Lee, Joongu (Rural Research Institute, Korea Rural Community Corporation) ;
  • Choi, Won (Department of Rural Systems Engineering, Research Institute of Agriculture and Life Sciences, Seoul National University)
  • 투고 : 2019.03.27
  • 심사 : 2019.07.01
  • 발행 : 2019.07.31

초록

Recently, natural disasters due to abnormal climates are frequently outbreaking, and there is rapid increase of damage to aged agricultural infrastructure. As agricultural infrastructure facilities are in contact with water throughout the year and the number of them is significant, it is important to build a maintenance management system. Especially, the current maintenance management system of pumping and drainage stations among the agricultural facilities has the limit of lack of objectivity and management personnel. The purpose of this study is to develop a performance evaluation model using the factors related to performance degradation of pumping and drainage facilities and to predict the performance of the facilities in response to climate change. In this study, we focused on the pumping and drainage stations belonging to each climatic zone separated by the Korea geographical climatic classification system. The performance evaluation model was developed using three different statistical models of POLS, RE, and LASSO. As the result of analysis of statistical models, LASSO was selected for the performance evaluation model as it solved the multicollinearity problem between variables, and showed the smallest MSE. To predict the performance degradation due to climate change, the climate change response variables were classified into three categories: climate exposure, sensitivity, and adaptive capacity. The performance degradation prediction was performed at each facility using the developed performance evaluation model and the climate change response variables.

키워드

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Fig. 1 The group without notable performance degradation

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Fig. 2 The group with notable performance degradation

Table 1 Selection of study sites according to geographical classification system in Korea

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Table 2 Variables and Role Relationships between Response Variability and Vulnerability of Climate Change regarding to Pumping Stations

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Table 4 Selection of parameters for meteorological data analysis

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Table 5 Result of statistical analysis by POLS, RE, and LASSO regression method

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Table 6 Test results of performance evaluation model through statistical analysis

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Table 7 Performance evaluation score prediction using performance evaluation model

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Table 3 Input/output variables related to the performance evaluation model

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참고문헌

  1. Choi, J. M., 2017. Creation of Korean standard weather data of 70 stations for securing reliability of a building energy evaluation and its Globalization, 6-89. Ministry of Land, Infrastructure and Transport.
  2. Choi, C. H., J. S. Kim, J. H. Kim, H. Y. Kim, W. J. Lee, and H. S. Kim, 2017. Development of heavy rain damage prediction function using statistical methodology. Journal of the Korean Society of Hazard Mitigation 17(3): 331-338 (in Korean). doi:10.9798/KOSHAM.2017.17.3.331.
  3. Choi, W., H. J. Kim, S. S. Yoon, J. O. Kim, N. S. Jung, H. J. Lee, Y. C. Han, and J. J. Lee, 2008. Survey for the management of reservoirs under control of local authorities of reservoir of city.gun in Korea. Journal of the Korean Society of Agricultural Engineers 50(3): 31-41 (in Korean). https://doi.org/10.5389/KSAE.2008.50.3.031
  4. Efron, B., T. Hastie, I. Johnstone, and R. Tibshirani, 2004. Least angle regression. Journal of the Annals of Statistics 32(2): 407-499. https://doi.org/10.1214/009053604000000067
  5. Han, C. R., 2017. Lectures on Panel Data Analysis. Seoul: Bakyoungsa.
  6. Hastie, T., J. Friedman, R. Tibshirani, 2001. The Elements of Statistical Learning [electronic resource] : Data Mining, Inference, and Prediction. New York, NY : Springer New York : Imprint: Springer.
  7. IPCC, 2014. Climate change 2014: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. Geneva, Switzerland: IPCC.
  8. Kim, H. D., 2018. Development of stability evaluation and management technique for agricultural production infrastructure due to climate change impacts, 7-172. Sejong: Ministry of Agriculture, Food and Rural Affairs.
  9. Kim, J. O., H. J. Kim, J. J. Lee, and M. K. Ko, 2002. Supporting system far safe appraisal and management of agricultural structures using relational database and geographic information. Journal of the Korean Society of Agricultural Engineers 44(3): 101-110 (in Korean).
  10. Kim, K. S., 1976. Climate of Korea. Seoul: ilmunsa.
  11. Kim, S. J., S. J. Bae, J. Y. Choi, S. P. Kim, S. K. Eun, S. H. Yoo, T. I. Jang, N. Y. Goh, S. W. Hwang, S. J. Kim, T. S. Park, K. H. Jeong, and S. H. Song, 2018. Analysis on the impact of climate change on the survey of rural water district and agricultural production infrastructure. Journal of the Korean Society of Agricultural Engineers 60(5): 1-15 (in Korean). doi:10.5389/KSAE.2018.60.5.001.
  12. Kim, S. J., S. M. Kim, and S. M. Kim, 2013. A study on the vulnerability assessment for agricultural infrastructure using principal component analysis. Journal of the Korean Society of Agricultural Engineers 55(1): 31-38 (in Korean). doi:10.5389/KSAE.2013.55.1.031.
  13. Kim, Y. S., K. M. Shin, M. P. Jung, I. T. Choi, and K. K. Kang, 2016. Classification of agroclimatic zones considering the topography characteristics in South Korea. Journal of Climate Change Research 7(4): 507-512 (in Korean). doi: 10.15531/ksccr.2016.7.4.507.
  14. Korea Meteorological Administration, Domestic climate data. http://www.weather.go.kr. Accessed 5 Nov. 2018.
  15. Korea Meteorological Administration, RCP Climate Change Scenario. http://www.climate.go.kr. Accessed 5 Nov. 2018.
  16. Korea Rural Community Corporation, 2018. Statistical yearbook of land and water development for agriculture 2017, 462-463. Naju, South Jeolla, Korea.
  17. Lee, C. B., N. S. Jung, S. K. Park, and S. O. Jeon, 2015. A study on the typology of agricultural reservoir for effective safety inspection systems. Journal of the Korean Society of Agricultural Engineers 57(5): 89-99 (in Korean). doi:10.5389/KSAE.2015.57.5.089.
  18. Lee, J. G., M. W. Kim, and T. H. Shin, 2011. Assessment of Appropriate Period and Cost(P&C) of Repair and Improvement for Irrigational Structures. Journal of Korean National Committee on Irrigation and Drainage 18(2): 142-160 (in Korean).
  19. Lee, J. J., 2011. Integrated safety management system for agricultural infrastructure in response to climate change. Rural Resources 53(3): 2-8 (in Korean).
  20. Lee, S. H., I. H. Heo, K. M. Lee, and W. T. Kwon, 2005. Classification of local climatic regions in Korea. Asia-Pacific Journal of Atmospheric Sciences 41(6): 983-995 (in Korean).
  21. Min, I. S., and P. S. Choi, 2009. STATA panel data analysis. Seoul, The Korean Association of STATA.
  22. Ministry of Agriculture, Food and Rural Affairs, 2017. Ordinance on management of agricultural production infrastructure (7 Dec. 2017), Sejong, Korea.
  23. Myeong, S. J., and D. G. Lee, 2009, Assessing vulnerability to climate change of the physical infrastructure in Korea through a survey of professionals. Journal of Environmental Impact Assessment 18(6): 347-357 (in Korean).
  24. Myung, S. J., 2009. Assessing vulnerability to climate of the physical infrastructure in Korea and developing adaptation strategies I, 8-109. Seoul: Korea Environment Institute.
  25. Myung, S. J., 2010. Assessing vulnerability to climate of the physical infrastructure in Korea and developing adaptation strategies II, 45-92. Seoul: Korea Environment Institute.
  26. Nau, R., 2019. What's the bottom line? How to compare models. https://people.duke.edu/-rnau/compare.htm. Accessed 7 May. 2019.
  27. Park, K. T., 2017. Development of evaluation techniques for performance-based management and operation of SOC facilities in Korea, 469-519. Anyang, Gyeonggi: Korea Agency for Infrastructure Technology Advancement.
  28. RAWRIS (Rural Agricultural Water Resource Information System), http://rawris.ekr.or.kr. Accessed 29 Oct. 2018.
  29. RIMS (Rural Infrastructure Management System), http://rims.ekr.or.kr. Accessed 29 Oct. 2018.
  30. Singh, A., 2019. Evaluation metrics for regression models-MAE vs MSE vs RMSE vs RMSLE. https://akhilendra.com/evaluation-metrics-regression-mae-mse-rmse-rmsle/. Accessed 8 May. 2019.
  31. Yoon, K. S., 2017. Establishment database of safety diagnosis history and safety diagnosis for agricultural infrastructure, 178-338. Ansan, Gyeonggi: Korea Rural Community Corporation.