人民长江
人民長江
인민장강
YANGTZE RIVER
2015年
8期
19-22,39
,共5页
马天儒%张行南%夏达忠%张慧
馬天儒%張行南%夏達忠%張慧
마천유%장행남%하체충%장혜
感潮河段%潮位预报%门限交互式回归%洪潮综合影响
感潮河段%潮位預報%門限交互式迴歸%洪潮綜閤影響
감조하단%조위예보%문한교호식회귀%홍조종합영향
tidal reach%tidal level forecast%threshold interactive regression%comprehensive interaction of flood and tide
感潮河段水流情势复杂,河道冲淤多变,地形资料获取困难。依据潮位变化成因,结合统计分析方法,提出利用门限交互式回归模型 TIR 对特征潮位进行预报。TIR 模型将交互式回归置于门限类模型框架中,避免洪潮要素综合过程中线性叠加假定的限制和缺陷,同时考虑洪潮驱动要素的时空差异及潮位非线性变化特征。依据门限分段理论,针对特定潮位站的不同水位等级,分别建立了不同的潮位预报相关关系。将 TIR 模型分别应用于南京、镇江及江阴3个潮位站,结果表明,各站预报效果良好。门限交互式回归模型框架合理、适应性强、资料依赖程度低、可获得较高的预报精度。
感潮河段水流情勢複雜,河道遲淤多變,地形資料穫取睏難。依據潮位變化成因,結閤統計分析方法,提齣利用門限交互式迴歸模型 TIR 對特徵潮位進行預報。TIR 模型將交互式迴歸置于門限類模型框架中,避免洪潮要素綜閤過程中線性疊加假定的限製和缺陷,同時攷慮洪潮驅動要素的時空差異及潮位非線性變化特徵。依據門限分段理論,針對特定潮位站的不同水位等級,分彆建立瞭不同的潮位預報相關關繫。將 TIR 模型分彆應用于南京、鎮江及江陰3箇潮位站,結果錶明,各站預報效果良好。門限交互式迴歸模型框架閤理、適應性彊、資料依賴程度低、可穫得較高的預報精度。
감조하단수류정세복잡,하도충어다변,지형자료획취곤난。의거조위변화성인,결합통계분석방법,제출이용문한교호식회귀모형 TIR 대특정조위진행예보。TIR 모형장교호식회귀치우문한류모형광가중,피면홍조요소종합과정중선성첩가가정적한제화결함,동시고필홍조구동요소적시공차이급조위비선성변화특정。의거문한분단이론,침대특정조위참적불동수위등급,분별건립료불동적조위예보상관관계。장 TIR 모형분별응용우남경、진강급강음3개조위참,결과표명,각참예보효과량호。문한교호식회귀모형광가합리、괄응성강、자료의뢰정도저、가획득교고적예보정도。
The flow regime and sediment deposition - scouring in a tidal reach are complicated,so it is difficult to obtain the topographical data of a tidal channel. According to the causes of tidal level variation,by using the statistical analysis technique, the threshold interactive regression model is put forward to forecast the tidal level. The threshold interactive regression model places interactive regression into threshold model framework,which can avoid the limitation and default of the linear superposition assumption in a flood - tidal integration process and can consider the spatial and temporal differences of tidal driving factors and the nonlinear variation characteristics of tidal level. According to threshold sectional theory,the staged tidal level forecasting rela-tionships are established for different tidal level stages of some selected tidal stations. The model has been used in three hydrolog-ical stations(Nanjing,Zhenjiang and Jiangyin)respectively. The results showed that the model works well in each station. With reasonable framework and strong adaptation,the TIR model can achieve good performance in forecasting and have low dependence to data. This model has practical significance and promotion value.