Improvement of a Predictive Data-Driven Model for Rainfall-Runoff Global Characterization of Aa River (original) (raw)

2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)

Abstract

An implementation of a precise flood forecasting is becoming increasingly urgent considering the growing number of climatic disasters. Several solutions of water asset management based or not on optimization and control exist but they require primarily on a good prediction of the volumes triggered by rainfall after a storm. Numerous studies are dedicated to this challenging issue that have this main objective of how to predict the runoff by using the right model with taking into account the nonlinearities induced by the geographical features of the catchment. This paper present an alternative to the large scale hydrological mathematical model based on offline and recursive/online parameter estimation of data-driven linear and nonlinear models. The updating of the model parameters allows to handle the variations due to the past rainfall quantities and to the weather forecasting.

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