CAJ | 학술논문

定量的河流水体中氮浓度预测方法有很多种,如何优选出预测精度较高的方法一直是学术界多年来致力于研究的重点.本研究采用因子分析法对预测方法的精度评价指标进行分析,并建立了预测方法精度的评价模型,对回归分析法、神经网络法、灰色系统法和增长率统计法4种水体氮浓度预测方法进行综合评估,优选出精度较高的河流水体氮浓度预测模型--BP神经网络颅测模型.结果表明,此评估模型对类似研究具有一定的参考价值,能为选择出合适的河流水体氮浓度预测方法提供依据.
정량적하류수체중담농도예측방법유흔다충,여하우선출예측정도교고적방법일직시학술계다년래치력우연구적중점.본연구채용인자분석법대예측방법적정도평개지표진행분석,병건립료예측방법정도적평개모형,대회귀분석법、신경망락법、회색계통법화증장솔통계법4충수체담농도예측방법진행종합평고,우선출정도교고적하류수체담농도예측모형--BP신경망락로측모형.결과표명,차평고모형대유사연구구유일정적삼고개치,능위선택출합괄적하류수체담농도예측방법제공의거.
There are many quantitative forecast models to predict river water nitrogen concentration, it is a key issue for research in academia field at present how to select a forecast model with higher estimate accuracy. The paper analyzes evaluation indexes of model accuracy by using factor analysis method, sets up an evaluation model of forecast accuracy. Then it predicts river water nitrogen concentration by using regression analysis, neural network method, grey system and growth rate statistic method, and carries on a comprehensive evaluation to the four kinds of forecast models by using evaluation model based on factor analysis method. It shows that BP neural network is a good forecast method to accurately predict river water nitrogen content than other three kinds. The results indicate that the evaluation model based on factor analysis method has a reference value in the similar studies, and it can provide evidence for selecting the suitable forecast models.