| Title |
ELM based Short-term Water Demand Prediction for Effective Operation of Water Treatment Plant |
| Authors |
Gee-Seon Choi ; Dong-Hoon Lee ; Sung-Hwan Kim ; Kyung-Woo Lee ; Myung-Geun Chun |
| Keywords |
ELM ; Water Demand Prediction ; Water Treatment Plant |
| Abstract |
In this paper, we develop an ELM(Extreme Learning Machine) based short-term 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 2007～2008 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. |