This study was carried out to evaluate the artificial neural network algorithm for water quality forecasting in Chungju lake, north Chungcheong province. Multi-layer perceptron(MLP) was used to train artificial neural networks. MLP was composed of one input layer, two hidden layers and one output layer. Transfer functions of the hidden layer were sigmoid and linear function. The number of node in the hidden layer was decided by trial and error method. It showed that appropriate node number in the hidden layer is 10 for pH training, 15 for DO and BOD, respectively. Reliability index was used to verify for the forecasting power. Considering some outlying data, artificial neural network fitted well between actual water quality data and computed data by artificial neural networks.

• 정효준(서울대학교 보건대학원 환경보건학과) | Hyo-Joon Jeong Corresponding Author
• 이소진(서울대학교 보건대학원 환경보건학과) | So-Jin Lee
• 이홍근(서울대학교 보건대학원 환경보건학과) | Hong-Keun Lee