A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy

A Modified Multi Time Step Integration for Dynamic Analysis

In this paper new implicit higher order accuracy (N-IHOA) time integration based on assumption of constant time step is presented for dynamic analysis. This method belongs to the category of the multi time step integrations. Here, current displacement and velocity are assumed to be functions of the velocities and accelerations of several previous time steps, respectively. This definition causes...

Data-driven models for monthly streamflow time series prediction

C. L. Wu and K. W. Chau* 2 Dept. of Civil and Structural Engineering, Hong Kong Polytechnic University, 3 Hung Hom, Kowloon, Hong Kong, People’s Republic of China 4 5 *Email: [email protected] 6 ABSTRACT 7 Data-driven techniques such as Auto-Regressive Moving Average (ARMA), K-Nearest-Neighbors (KNN), and 8 Artificial Neural Networks (ANN), are widely applied to hydrologic time series predi...

Multiscale streamflow forecasting using a new Bayesian Model Average based ensemble multi-wavelet Volterra nonlinear method

Optimal Multi-model Ensemble Method in Seasonal Climate Prediction

Given a large number of dynamical model predictions, this study endeavors to improve seasonal climate prediction through optimizing multi-model ensemble (MME) method. We have developed a new MME method and evaluated it using 15 dynamical models’ retrospective forecasts for the period 1981-2001 in comparison with other MME methods. The strengths of the new method lie in a statistical error corre...

A Modified Stacking Ensemble Machine Learning Algorithm Using Genetic Algorithms

Distributed data mining and ensemble learning are two methods that aim to address the issue of data scaling, which is required to process the large amount of data collected these days. Distributed data mining looks at how data that is distributed can be effectively mined without having to collect the data at one central location. Ensemble learning techniques aim to create a meta-classifier by c...