CAJ | 학술논문

　　冬季冰期水温的预报是冰情预报的基础。将基于自适应网络的模糊推理系统（ANFIS）和Levenberg-Mar-quardt算法改进的BP神经网络模型ANN应用到黄河冬季水温预报中，通过分析模型结构特性、水文数据及其相关预报因子的特点，确定模型合理的输入参数。在ANFIS和ANN模型输入因子和预见期相同的条件下，预报黄河最北端三湖河口、头道拐、巴彦高勒3个水文站冬季结冰期的水温。两种模型预报结果的优劣通过确定性系数、均方根误差和相关系数3种参数的比较进行评定。通过12组参数预报结果的比较和特性评定，自适应网络的模糊推理系统预报结果均比神经网络模型预报结果好。研究表明：基于自适应网络的模糊推理系统这一新的理论能够适合冬季结冰期水温预报的特点，预报精度得到普遍的提高。
　　동계빙기수온적예보시빙정예보적기출。장기우자괄응망락적모호추리계통（ANFIS）화Levenberg-Mar-quardt산법개진적BP신경망락모형ANN응용도황하동계수온예보중，통과분석모형결구특성、수문수거급기상관예보인자적특점，학정모형합리적수입삼수。재ANFIS화ANN모형수입인자화예견기상동적조건하，예보황하최북단삼호하구、두도괴、파언고륵3개수문참동계결빙기적수온。량충모형예보결과적우렬통과학정성계수、균방근오차화상관계수3충삼수적비교진행평정。통과12조삼수예보결과적비교화특성평정，자괄응망락적모호추리계통예보결과균비신경망락모형예보결과호。연구표명：기우자괄응망락적모호추리계통저일신적이론능구괄합동계결빙기수온예보적특점，예보정도득도보편적제고。
Forecast on freeze-up water temperature is a basis for the ice condition. In this study, Adap-tive-Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are applied to the freeze-up water temperature forecasting in the Yellow River. Reasonable input parameters of the two models are determined through analyzing each model characteristic,information of water temperature and re-lated factors. For better comparability, the same input factors and forecast periods are used to estimate the 4-year water temperatures in Bayangaole, Shanhuhekou and Toudaoguai hydrometric stations, which are lo-cated in the most north of the Yellow River. The forecast results are assessed by coefficients of determina-tion, correlation coefficients and root mean square errors. A comparison of ANFIS and ANN results shows that ANFIS gave better results than ANN by 12 forecast cases. As a result, ANFIS model is founds to be superior to ANN model for forecasting the time series information,such as freeze-up water temperature.