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

Abstract

► 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. 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.