利用回歸和人工神經(jīng)網(wǎng)絡預測城市雨水徑流總磷模型的有效性

摘要： 利用人工神經(jīng)網(wǎng)絡(ANN),探討在不無監(jiān)測系統(tǒng)的集水區(qū)城市降水質量預測的適用性.預測使用常規(guī)的氣候和地理數(shù)據(jù)集,通過構建背景傳播的神經(jīng)網(wǎng)絡和回歸聯(lián)合模型,克服利用逐步回歸的方法對數(shù)據(jù)進行分析時違背獨立數(shù)據(jù)假設的問題.研究通過交叉驗證用于確定停止降水時間為輸入變量參數(shù),利用地區(qū)平均濃度(EMC)作為獨立的變量,構建的模型比用負荷量構建的模型更精確.數(shù)據(jù)域和輸入變量的選擇對回歸模型的準確性也有較大影響.但計算效率、動量和隱節(jié)點數(shù)目的選擇等因素,對人工神經(jīng)網(wǎng)絡模型準確性的影響較小.同時,回歸和人工神經(jīng)網(wǎng)絡模型的降水質量預測結果十分相似,但在不無監(jiān)測系統(tǒng)的集水區(qū)域城市降水質量的預測方面,回歸模型更有實效性. Abstract： This paper investigates the applicability of using artificial neural networks (ANNs) to predict urban stormwater quality at unmonitored catchments.Back-propagation neural networks and regression models were constructed using a set of general climatic and geographic data.Violation of the assumption of data independence lead to the inclusion of insignificant variables when the data was analysed using stepwise regression.To overcome this problem, cross validation was used to determine when to cease input variable entry.Models constructed using event mean concentration (EMC) as the dependent variable were more accurate than those using load.The data domain and selection of input variables had a significant effect upon the accuracy of the regression models.Whereas the choice of learning rates, momentum and number of hidden nodes had an insignificant effect upon the accuracy of the ANN models.Regression and ANN models yielded similar predictions.However, the efficiency of the regression models made them a more pragmatic approach for predicting urban stormwater quality at unmonitored sites. 作 者： 梅·D    西瓦庫瑪·M    MAY D    SIVAKUMAR M   作者單位： 澳大利亞伍龍貢大學環(huán)境工程,新南威爾士州,伍龍貢,2500  期 刊： 沈陽化工學院學報    Journal： JOURNAL OF SHENYANG INSTITUTE OF CHEMICAL TECHNOLOGY  年，卷(期)： 2010, 24(1)  分類號： X820.4  關鍵詞： 人工神經(jīng)網(wǎng)絡    城市雨水水質    總磷    模型    Keywords： artificial neural networks    urban stormwater quality    phosphorus    model   

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