Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube

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• 作者：Anita Csá ; brá ; gi^a ; ^{csabragi.anita@gmail.com} ; ^{csabragi.anita@gek.szie.hu} ; Sá ; ndor Molná ; r^a ; ^{molnar.sandor@gek.szie.hu} ; Pé ; ter Tanos^a ; ^{tanospeter@gmail.com} ; Jó ; zsef Ková ; cs^b ; ^{kevesolt@gmail.com}
• 关键词：ANN ; Artificial Neural Network ; CA ; CB ; CC ; CD ; Combination A ; B ; C ; D ; DO ; dissolved oxygen ; EC ; electrical conductivity ; GRNN ; General Regression Neural Network ; hydro PP ; hydro power plant ; HNPP ; Hungarian Nuclear Power Plant ; IA ; Willmott&rsquo ; s index of agreement ; MAE ; mean absolute error ; MLPNN ; Multilayer Perceptron Neural Network ; MLR ; Multivariate Linear Regression ; R2 ; coefficient of determination ; RBFNN ; Radial Basis Function Neural Network ; RF ; runoff ; rkm ; river kilometres ; RMSE ; r
• 刊名：Ecological Engineering
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：100
• 期：Complete
• 页码：63-72
• 全文大小：1923 K
• 卷排序：100

文摘

Forecasting dissolved oxygen in a river using artificial neural networks (ANN). ANNs: Multilayer Perceptron NN, Radial Basis Function NN & General Regression NN. Comparison of the possible modelling of areas with different ecological status. Sensitivity analysis showed that pH is the most important variable.