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RISKnowledge Extraction from Trained Neural Network River Flow Models
Publisher: American Society of Civil Engineers
Abstract: Artificial neural networks (ANNs), due to their excellent capabilities for modeling complex processes, have been successfully applied to a variety of problems in hydrology. However, one of the major criticisms of ANNs is ...
Radial Basis Function Neural Network for Modeling Rating Curves
Publisher: American Society of Civil Engineers
Abstract: The establishment of a rating curve is an important problem in hydrology. Generally, a regression approach is applied to establish the relationship between stage and discharge. However, this approach fails in the cases ...
Discussion of “Performance of Neural Networks in Daily Streamflow Forecasting” by S. Birikundavyi, R. Labib, H. T. Trung, and J. Rousselle
Publisher: American Society of Civil Engineers
Fitting of Hydrologic Models: A Close Look at the Nash–Sutcliffe Index
Publisher: American Society of Civil Engineers
Abstract: Quantitative assessments of the degree to which the modeled behavior of a system matches with the observations provide an evaluation of the model’s predictive abilities. In this context, the Nash–Sutcliffe efficiency index ...
Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique
Publisher: American Society of Civil Engineers
Abstract: This paper examines the potential of artificial neural networks (ANN) in estimating the actual crop evapotranspiration (ET) from limited climatic data. The study employed radial-basis function (RBF) type ANN for computing ...
Closure to “Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique” by K. P. Sudheer, A. K. Gosain, and K. S. Ramasastri
Publisher: American Society of Civil Engineers