کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4577581 | 1630021 | 2011 | 14 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France) Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)](/preview/png/4577581.png)
SummaryA neural network model is applied to simulate the rainfall-runoff relation of a karst spring. The input selection for such a model becomes a major issue when deriving a parsimonious and efficient model. The present study is focused on these input selection methods; it begins by proposing two such methods and combines them in a subsequent step. The methods introduced are assessed for both simulation and forecasting purposes. Since rainfall is very difficult to forecast, especially in the study area, we have chosen a forecasting mode that does not require any rainfall forecast assumptions. This application has been implemented on the Lez karst aquifer, a highly complex basin due to its structure and operating conditions. Our models yield very good results, and the forecasted discharge values at the Lez spring are acceptable up to a 1-day forecasting horizon. The combined input selection method ultimately proves to be promising, by reducing input selection time while taking into account: (i) the model’s ability to accommodate nonlinearity and (ii) the forecasting horizon.
► Forecasting flash flood of a karst aquifer in Mediterranean context is carried out.
► Complexity selection method deriving a parsimonious neural network is proposed.
► Outflow forecasting one day ahead is performed without rainfall assumption.
► The neural model is able to extrapolate on the most intense event of the database.
Journal: Journal of Hydrology - Volume 403, Issues 3–4, 17 June 2011, Pages 367–380