ARIMA 모형과 인공신경망모형의 BOD예측력 비교

• 영문명: Comparison of the BOD Forecasting Ability of the ARIMA model and the Artificial Neural Network Model
• 발행기관: 한국환경보건학회
• 저자명: 정효준 이홍근
• 간행물 정보: 『1. 한국환경보건학회지』제28권 제3호, 19~25쪽, 전체 7쪽
• 주제분류: 공학 > 환경공학
• 파일형태: PDF
• 발행일자: 2002.09.30

영문 초록

In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)12, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

목차

ABSTRACT

Ⅰ.서론

Ⅱ.연구방법

Ⅲ.결과 및 고찰

Ⅳ.결론

참고문헌

키워드

ARIMA Artificial neural networks Back-propagation Forecasting