Generalized Regression Neural Network Based Approach as a New Tool for Predicting Total Dissolved Gas (TDG) Downstream of Spillways of Dams: a Case Study of Columbia River Basin Dams, USA

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• 作者：Salim Heddam
• 关键词：Prediction ; Total dissolved gas ; Spillway ; Generalized regression neural network ; Multiple linear regression
• 刊名：Environmental Processes
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：4
• 期：1
• 页码：235-253
• 全文大小：
• 刊物类别：Environmental Science and Engineering; Environmental Management; Waste Management/Waste Technology;
• 刊物主题：Environmental Science and Engineering; Environmental Management; Waste Management/Waste Technology; Water Quality/Water Pollution;
• 出版者：Springer International Publishing
• ISSN：2198-7505
• 卷排序：4

文摘

A generalized regression neural network (GRNN) has been applied to estimate total dissolved gas uptake (Δ_TDG) that corresponds to the net difference between TDG at the tailwater and TDG in the forebay of dams, by using available measured data. To demonstrate the capability and robustness of the GRNN model, we used data for a period of 2 years: 2015 and 2016. For each year, we selected a 6-month period, from April to September, which corresponds to the spilling season. The data were available from two stations which operated simultaneously by the United States Geological Survey (USGS) and the U.S. Army Corps of Engineers (USACE): USGS ID 454249120423500 station at Columbia River, right bank, near Cliffs, Washington (John Day TailWater), and USGS ID 14105700 station at Columbia River at The Dalles, Oregon (The Dalles TailWater). For developing the models, we used six input variables measured at hourly time step: total dissolved gas measured in the forebay of the dam (TDG_F), water temperature (TE), barometric pressure (BP), spill from dam (SP), sensor depth (SD), and total flow (TF). The performances of the models were evaluated using the root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE) and correlation coefficient (R) statistics. The proposed GRNN model was compared to the multiple linear regression (MLR) with respect to their capability of modelling TDG, using several combinations of the input variables. According to the results obtained, it was found that: (i) the Δ_TDG could be successfully estimated using the GRNN model; and (ii) the GRNN M1 model, which uses all the six input variables is the best model among all others tested for the two stations.