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

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.

Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France)

“…For this reason, neural network modelling seems to be a relevant method (Kong-A-Siou et al, 2011Kurtulus and Razack, 2007). For this purpose, in recent decades, the multilayer perceptron has been increasingly used in the field of hydrology (Maier and Dandy, 2000;Toth, 2009).…”

“…These models have been effective in identifying the rainfall-run-off relationship (Hsu et al, 1995). Their ability to forecast flash floods (Toukourou et al, 2011;Artigue et al, 2012) and model karst system behaviour have also been demonstrated (Kong-A-Siou et al, 2011). To model hydrosystem behaviour efficiently, neural networks need relevant data sets as input and output variables, and rigorous application of regularisation methods (Abrahart and See, 2007;Bowden et al, 2005;Fernando et al, 2009).…”

Neural network modeling and geochemical water analyses to understand and forecast karst and non-karst part of flash floods (case study on the <i>Lez</i> river, Southern France)

“…Nevertheless, the karst contribution is significant and can worsen significantly the flood. We thus propose a methodology able to estimate separately karst flood and surface flood in order to design two different predictors, for example with neural networks models, as shown by Kong-A-Siou et al (2011a). The methodology proposes several steps each one achieved and described in this paper in a specific section: (i) establishment of the conceptual model of the basin behaviour (surface and karst), (ii) chemical analysis in order to quantify karst and non karst water, at key points of the basin, were floods don't prevent from making measurements in safe conditions, (iii) propagation of karst and non karst flood up to the outlet of the watershed, and finally (iv) reconstitution of the both karst and non karst floods.…”

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

“…Artificial neural networks are imitating human brain by using mathematical methods and have been proven to be beneficial tools for simulating, predicting and forecasting hydrological variables (Nadiri 2007;Nourani et al 2008b;Piotrowski and Napiorkowski 2011;Siou et al 2011;. The most widely used neural network is the multi-layer perceptron (MLP) (Hornik et al 1989;Haykin 1999;Sulaiman et al 2011;Fijani et al 2012;Mustafa et al 2012).…”