Journal of the Korean Institute of Illuminating and Electrical Installation Engineers (조명전기설비학회논문지)

The Korean Institute of IIIuminating and Electrical Installation Engineers (한국조명전기설비학회)
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ELM based short-term Water Demand Prediction for Effective Operation of Water Treatment Plant

정수장 운영효율 향상을 위한 ELM 기반 단기 물 수요 예측

  • Published : 2009.09.30
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Abstract

In this paper, we develop an ELM(Extreme Learning Machine) based short-tenn water demand prediction algorithm which solves overfitting problem of MLP(Multi Layer Perceptron) and has quick training time. To show effectiveness of proposed method, we analyzed time series data collected in A water treatment plant at Chung-Nam province during $2007{\sim}2008$ years and used the selected data for the verification of developed algorithm. According to the experimental results, MLP model showed 5.82[%], but the proposed ELM based model showed 5.61[%] with respect to MAPE, respectively. Also, MLP model needed 7.57s training time, but ELM based model was 0.09s. Therefore, the proposed ELM based short-term water demand prediction model can be used to operate the water treatment plant effectively.

본 논문에서는 단기 물 수요 예측에 대한 모델구현을 위해 MLP의 과도학습 문제를 해결할 수 있고 빠른 학습이 가능한 ELM 기반 단기 물 수요 예측 알고리즘을 제안한다. 제시된 알고리즘의 검증을 위해 2007년도와 2008년도 충남지역 광역상수도인 A정수장에서 취득된 데이터를 분석하여 알고리즘 구현의 정확도 분석에 사용하였다. 실험 결과 MLP모델은 MAPE가 5.82[%]인 반면, 제안된 방법인 ELM기반 모델은 5.61[%]로 성능이 향상된 것으로 나타났다. 또한, MLP모델은 학습에 소요된 시간이 7.57초인 반면, ELM 기반 모델은 0.09초로 빠른 학습이 가능함을 알 수 있었다. 따라서 제안된 ELM 기반 알고리즘은 정수장의 효율적 운영을 위한 단기 물 수요 예측에 활용할 수 있음을 보였다.

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References

  1. Kyung-Hoon Rhee, IL-Hwan Kang, Byoung-Seok Moon,"Application of ANFIS for Prediction of Daily Water Supply" Journal of the Korean Society of Water and Wastewater, Vol. 14, No. 3, pp. 281-290, 2000
  2. Kyung-Hoon Rhee, IL-Hwan Kang, Chang-Ju Oh,"Forecasting of Urban Daily Water Demand by usingBackpropagation Algorithm Neural Network" Journal ofthe Korean Society of Water and Wastewater, Vol. 12,No. 4, pp. 43-52, 1998
  3. Shin-Geol Kim, Sin-Suk Pyon, Young-Sang Kim,Ja-Yong Koo, "Forecasting the Long-term Water DemandUsing System Dynamics in Seoul", Journal of the KoreanSociety of Water and Wastewater, Vol. 20, No. 2, pp.187-196, 2006
  4. Ishmael S. Msiza, Fulufhelo V. Nelwamondo, TshilidziMarwala, Water Demand Prediction using Artificial NeuralNetworks and Support Vector Regression," JOURNAL OFCOMPUTERS, Vol. 3, No. 11, pp. 1-8, NOVEMBER 2008
  5. Mehmet Ali Yurdusev, Mahmut Firat, "Adaptive neurofuzzy inference system approach for municipal waterconsumption modeling: An application to Izmir, Turkey,"Journal of Hydrology. Vol. 365, pp. 225-234, 2009 https://doi.org/10.1016/j.jhydrol.2008.11.036
  6. Ministry of Environment, Standard of waterworksfacilities, KWWA, 2004
  7. G. B. Huang, Q. Y. Zhu, and C. K. Siew, "Extremelearning machine: a new learning scheme of feedforwardneural netowrks," in Proc. 2004 IEEE Int. Conf. NeuralNetworks, Vol. 2, pp. 985-990, 2004 https://doi.org/10.1109/IJCNN.2004.1380068
  8. G. B. Huang, Q. Y. Zhu, and C. K. Siew, "Extremelearning machine: theory and applications,"Neurocomputing, Vol. 70, No. 1-3, pp. 489-501, 2006 https://doi.org/10.1016/j.neucom.2005.12.126
  9. Jae-Hoon Cho, Dae-Jong Lee, Myung-Geun Chun,"Parameter Optimization of Extreme Learning MachineUsing Bacterial Foraging Algorithm," Journal of KoreanInstitute Intelligence System, Vol. 17, pp. 807-812, 2007 https://doi.org/10.5391/JKIIS.2007.17.6.807
  10. G. B. Huang, L. Chen, and C. Siew, "UniversalApproximation Using Incremental ConstructiveFeedforward Networks With Random Hidden Nodes," IEEETransactions on Neural Networks, Vol. 17, pp. 879-892,2006 https://doi.org/10.1109/TNN.2006.875977
  11. G. B. Huang and L. Chen, "Enhanced random searchbased incremental extreme learning machine,"Neurocomputing, Vol. 71, pp. 3460-3468, 2008 https://doi.org/10.1016/j.neucom.2007.10.008

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