Fig. 1. Conceptual construction of proposal model.
Fig. 2. Process of calculating and verifying significance of multiple linear regression model.
Fig. 3. Pattern of operational data sensor. (a) Flow rate and (b) water level.
Fig. 4. The RMSE per sensor in each model prediction.
Fig. 5. Comparison chart of RMSE between models.
Table 1. Performance comparison between proposed method and existing method
참고문헌
- J. R. Kim, G. W. Shin, S. T. Hong, and G. H. Yoo, "A study on detecting algorithm for outlier data in water supply," in Proceedings of the Korean Institute of Communications and Information Sciences Summer Conference, 2016, pp. 327-328, 2016.
- J. H. Seok, J. J. Kim, J. Y. Lee, J. J. Lee, Y. J. Song, G. W. Shin, "Abnormal data refinement and error percentage correction methods for effective short-term hourly water demand forecasting," International Journal of Control, Automation and Systems, vol. 12, no. 6, pp 1245-1256, 2014. https://doi.org/10.1007/s12555-014-0001-z
- G. H. Yoo, J. R. Kim, and G. W. Shin, "A study on the cleansing of water data using LSTM algorithm," in Proceedings of the Korea Institute of Information and Communication Engineering Fall Conference, 2017, pp. 501-503, 2017.
- H. S. Kim, "Data cleansing algorithm for reducing outlier of water supply information," Ph.D. dissertation, Pai Chai University, Daejeon, Korea, 2018.
- E. J. Lee "Basic and applied research of CNN and RNN," Broadcasting and Media Magazine, vol. 22, no. 1, pp. 87-95, 2017.
- Y. H. Kim, Y. K. Hwang, T. G. Kang, and K. M. Jung, "LSTM language model based korean sentence generation," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 5, pp. 592-601, 2016. https://doi.org/10.7840/kics.2016.41.5.592
- G. D. Sim and J. H. Seok, "A study on the prediction interest rate using LSTM systems," in Proceedings of the Korean Institute of Communications and Information Sciences Winter Conference, 2018, pp. 308-309.
- C. Kim and J. W. Kang, "LSTM based anomaly detection on semiconductor manufacturing data," in Proceedings of the Korea Software Conference 2017, Busan, Korea, 2017, pp. 760-762.