Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)

Link to Full Text

Download Full Text

Publication Date

6-17-2011

Publication Title

Journal of Hydrology

Volume Number

403

Issue Number

3-4

Abstract

A 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.

Keywords

Flash flood, Modeling, Neural networks, Karst aquifer

Document Type

Article

Digital Object Identifier (DOI)

https://doi.org/10.1016/j.jhydrol.2011.04.015

Link to Full Text

Share

 
COinS