Work

Hourly Flow Forecasting in a Karst Watershed: The Iton River (France)

Year: 2025

Type: article

Abstract: Rainfall–runoff models are widely used in water management and flood forecasting. In this study, we present a rainfall–runoff model to forecast hourly flows based on an artificial neural network (ANN). This model was developed and applied to the Iton watershed (northwestern France) to solve the problems of nonlinearity in the rainfall–runoff relationship resulting from karst and complex hydrogeological behaviors. The model design required several steps during which we were able to identify the model parameters and create the database needed to perform the flow rate forecast. This work has resulted in an ANN model able to perform an efficient prediction up to a 48 h time horizon. These results confirm that ANN models can play an important role in forecasting the nonlinear rainfall–runoff relationship encountered in many watersheds.

Source: Water

DOI: 10.3390/w17070977

Authors: Ouissem Kharroubi, Raouf Achour, Mohamed-Tahar Ammami, Ahmed Benamar

Institutions: University of Jendouba, École Spéciale des Travaux Publics, Centre National de la Recherche Scientifique, Université Le Havre Normandie, Laboratoire Ondes et Milieux Complexes

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,137