本文選題：互信息 + 湖泊水位預(yù)測(cè)�。� 參考：《人民長(zhǎng)江》2017年16期

【摘要】：鑒于傳統(tǒng)的湖泊水位預(yù)測(cè)在輸入因子選擇時(shí)具有一定的盲目性,以西洞庭湖為例,利用基于互信息的輸入因子選擇法建立了日水位預(yù)測(cè)模型。按河流生態(tài)功能將水文年劃分為枯水期、汛前漲水期、汛期、汛后退水期4個(gè)時(shí)期,然后分期計(jì)算影響湖泊日水位的自變量與日水位的互信息,并引入廣義相關(guān)系數(shù)將互信息歸一化,選出各時(shí)期互信息最大的自變量因子作為模型的輸入變量。經(jīng)過模型計(jì)算與數(shù)據(jù)分析可得:F檢驗(yàn)結(jié)果顯著,回歸值與實(shí)測(cè)值的相關(guān)度高,剩余標(biāo)準(zhǔn)差小。由此證明用互信息篩選出的因子作為模型的輸入變量能取得較好的精度并在實(shí)際中易于操作。
[Abstract]:In view of the blindness of the traditional prediction of lake water level in the selection of input factors, an input factor selection method based on mutual information is used to establish a daily prediction model of water level in the west of Dongting Lake. According to the ecological function of rivers, the hydrological year is divided into four periods: dry season, flood period before flood and receding period, and then the mutual information between independent variables and daily water level affecting the daily water level of lakes is calculated by stages. Then the generalized correlation coefficient is introduced to normalize the mutual information, and the independent variable factor with the largest mutual information in each period is selected as the input variable of the model. Through the model calculation and data analysis, we can find that the result of the test is remarkable, the correlation between the regression value and the measured value is high, and the residual standard deviation is small. It is proved that the factors filtered out by mutual information as input variables of the model can obtain good accuracy and are easy to operate in practice.
【作者單位】： 武漢大學(xué)水資源與水電工程科學(xué)國(guó)家重點(diǎn)實(shí)驗(yàn)室;武漢大學(xué)水資源安全保障湖北省協(xié)同創(chuàng)新中心;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(51179130) 國(guó)家重點(diǎn)研發(fā)計(jì)劃課題(2016YFC0401306)
【分類號(hào)】：P338
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本文編號(hào)：1872758