Improving generalization of artificial neural networks in rainfall–runoff modelling / Amélioration de la généralisation de réseaux de neurones artificiels pour la modélisation pluie-débit

Abstract

Artificial neural networks (ANNs) are general-purpose techniques that can be used for nonlinear data-driven rainfall–runoff modelling. The key issue to construct a good model by means of ANNs is to understand their structural features and the problems related to their construction. Indeed, the quantity and quality of data, the type of noise and the mathematical properties of the algorithm for estimating the usual large number of parameters (weights) are crucial for the generalization performances of ANNs. However, it is well known that ANNs may suffer from poor generalization properties due to the high number of parameters and non-Gaussian data noise. Therefore, in the first part of this paper, the features and problems of ANNs are discussed. Eight Avoiding Overfitting Techniques are then presented, considering that these are methods for improving the generalization of ANNs. For this reason, they have been tested on two case studies—rainfall–runoff data from two drainage basins in the south of Italy—in order to gain insight into their properties and to investigate if there is one that absolutely gives the best performance.     

Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones

Abstract

The abilities of neuro-fuzzy (NF) and neural network (NN) approaches to model the streamflow–suspended sediment relationship are investigated. The NF and NN models are established for estimating current suspended sediment values using the streamflow and antecedent sediment data. The sediment rating curve and multi-linear regression are also applied to the same data. Statistic measures were used to evaluate the performance of the models. The daily streamflow and suspended sediment data for two stations—Quebrada Blanca station and Rio Valenciano station—operated by the US Geological Survey were used as case studies. Based on comparison of the results, it is found that the NF model gives better estimates than the other techniques.