Multi-step ahead forecasting is still an open challenge in time series forecasting. Several
approaches that deal with this complex problem have been proposed in the literature but an …

This paper reviews the application of artificial neural network (ANN) models to time series
prediction tasks. We begin by briefly introducing some basic concepts and terms related to …

Sensitivity analysis (SA) is a commonly used approach for identifying important parameters
that dominate model behaviors. We use a newly developed software package, a Problem …

Data visualization and feature selection methods are proposed based on the) oint mutual
information and ICA. The visualization methods can find many good 2-D projections for high …

Abstract Model parameter calibration is a fundamentally important stage that must be
completed before applying a model to address practical problems. In this study, we describe …

This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …

A feature/input selection method is proposed based on joint mutual information. The new
method is better than the existing methods based on mutual information in eliminating …

Based on an observation about the different effect of ensemble averaging on the bias and
variance portions of the prediction error, we discuss training methodologies for ensembles of …

An F77 package for feed-forward artificial neural network data processing, JETNET 3.0, is
presented. It represents a substantial extension and generalization of an earlier release …

Parameter optimization is an essential step in hydrological simulations, especially for
solving practical problems. However, parameter optimization is usually intractable for …