Prediction of total bed material load for rivers in Malaysia: A case study of Langat,Muda and Kurau Rivers

A soft computational technique is applied to predict sediment loads in three Malaysian rivers. The feed forward-back propagated (schemes) artificial neural network (ANNs) architecture is employed without any restriction to an extensive database compiled from measurements in Langat, Muda, Kurau different rivers. The ANN method demonstrated a superior performance compared to other traditional sediment-load methods. The coefficient of determination, 0.958 and the mean square error 0.0698 of the ANN method are higher than those of the traditional method. The performance of the ANN method demonstrates its predictive capability and the possibility of generalization of the modeling to nonlinear problems for river engineering applications.     

Correction to: Multivariate modeling of agricultural river water abstraction via novel integrated-wavelet methods in various climatic conditions

Environment, Development and Sustainability - The authors would like to apologize for inadvertent mistakes in this article.     

Multivariate modeling of agricultural river water abstraction via novel integrated-wavelet methods in various climatic conditions

Environment, Development and Sustainability - Water resource management in the agricultural sector is a necessity that profoundly impacts water resources conservation for future generations. With...