Wan Hanna Melini Wan Mohtar

TL;DR: In this paper, a hybrid adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach was proposed for monthly streamflow forecasting. But the results of the ANFIS-FFA model are compared with the classical ANFis model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy inference system (FIS).

TL;DR: In this paper, two different ANN algorithms, the feed forward neural network (FFNN) and radial basis function (RBF) have been used for estimating the daily sediment load.

TL;DR: All AI approaches that have been applied in sediment modelling are reviewed, and the current research focuses on the development of AI application in sediment transport.

TL;DR: Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks, and it is demonstrated that RBFNN model is superior to the FFNN forecasting model, andRBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.

TL;DR: Relatively good performance of the proposed hybrid models with a data pre-processing approach provides a successful alternative to achieve better accuracy in streamflow forecasting compared to the traditional artificial neural network approach without a dataPre-processing scheme.