BAG: A Linear-Nonlinear Hybrid Time Series Prediction Model for Soil Moisture

A hybrid linear-neural model for time series forecasting

This paper considers a linear model with time varying parameters controlled by a neural network to analyze and forecast nonlinear time series.We show that this formulation, called neural coefficient smooth transition autoregressive (NCSTAR) model, is in close relation to the threshold autoregressive (TAR) model and the smooth transition autoregressive (STAR) model with the advantage of naturall...

Which Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...

Application of Support Vector Machine Based on Time Series For Soil Moisture and Nitratenitrogen Content Prediction

Support Vector Machine based on Time Series (SVM-TS) was applied to predict soil moisture and nitrate nitrogen (NO3--N) content. For the prediction of soil moisture, the statistical result (t-test) indicate that there is no obvious difference between predicted and observed values in 0-20cm and 20-60cm soil layers, and that SVM-TS is capable for soil moisture prediction. For the prediction of NO...

Pol-SAR time series for soil moisture estimation under vegetation

In this paper the problem of soil moisture estimation under vegetation is investigated. Pol-SAR inversion algorithms are developed and applied on the multi-temporal AGRISAR data set from 2006 to investigate their potential and limitations over time. Modifications of the model based Freeman decomposition are used to decompose the scattering contributions and to invert the surface and dihedral sc...

Swelling Bulk Density and its Application in Arya and Paris Model for Soil Moisture Curve Prediction