Learning Sets of Sub-Models for Spatio-Temporal Prediction

2007 ◽  
pp. 123-136 ◽  
Author(s):  
Andrew Bennett ◽  
Derek Magee
Keyword(s):  
Temporal Prediction ◽  
Spatio Temporal ◽  
Learning Sets

Spatio-Temporal Prediction of Roadside PM2.5 based on Sparse Mobile Sensing and Traffic Information

2021 ◽  
Author(s):  
A. Kakarla ◽  
V. S. K. R. Munagala ◽  
T. Ishizaka ◽  
A. Fukuda ◽  
S. Jana
Keyword(s):  
Mobile Sensing ◽  
Traffic Information ◽  
Temporal Prediction ◽  
Spatio Temporal

scholarly journals Spatio-temporal prediction of shopping behaviours using taxi trajectory data

2018 ◽  
Author(s):  
John Cartlidge ◽  
Shuhui Gong ◽  
Ruibin Bai ◽  
Yang Yue ◽  
Qingquan Li ◽  
...  
Keyword(s):  
Trajectory Data ◽  
Temporal Prediction ◽  
Spatio Temporal ◽  
Taxi Trajectory

A Neural Network for Modeling Multicategorical Parcel Use Change

2013 ◽  
pp. 1297-1308
Author(s):  
Kang Shou Lu ◽  
John Morgan ◽  
Jeffery Allen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Multinomial Logistic Regression ◽  
Slight Variation ◽  
Ann Model ◽  
Temporal Prediction ◽  
Percentage Points ◽  
Spatio Temporal ◽  
Coastal County ◽  
Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.

A Neural Network for Modeling Multicategorical Parcel Use Change

2012 ◽  
pp. 131-142
Author(s):  
Kang Shou Lu ◽  
John Morgan ◽  
Jeffery Allen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Multinomial Logistic Regression ◽  
Slight Variation ◽  
Ann Model ◽  
Temporal Prediction ◽  
Percentage Points ◽  
Spatio Temporal ◽  
Coastal County ◽  
Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.

scholarly journals Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network

2019 ◽  
Vol 16 (20) ◽  
pp. 3788 ◽  
Author(s):  
Yu-ting Bai ◽  
Xiao-yi Wang ◽  
Qian Sun ◽  
Xue-bo Jin ◽  
Xiao-kai Wang ◽  
...  
Keyword(s):  
Time Series Prediction ◽  
Physical Structure ◽  
Prediction Algorithm ◽  
Industrial Park ◽  
Temporal Prediction ◽  
Blind Area ◽  
Nonlinear Relation ◽  
Spatio Temporal ◽  
Set Up ◽  
Temporal Dimensions

The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of “Circumjacent Monitoring-Blind Area Inference”. In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions.

A Neural Network for Modeling Multicategorical Parcel Use Change

2011 ◽  
Vol 2 (3) ◽  
pp. 20-31 ◽  
Author(s):  
Kang Shou Lu ◽  
John Morgan ◽  
Jeffery Allen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Multinomial Logistic Regression ◽  
Land Use Changes ◽  
Slight Variation ◽  
Ann Model ◽  
Temporal Prediction ◽  
Percentage Points ◽  
Spatio Temporal ◽  
Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.

scholarly journals Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

2011 ◽  
Vol 107 (1-2) ◽  
pp. 265-277 ◽  
Author(s):  
Tomislav Hengl ◽  
Gerard B. M. Heuvelink ◽  
Melita Perčec Tadić ◽  
Edzer J. Pebesma
Keyword(s):  
Time Series ◽  
Temporal Prediction ◽  
Spatio Temporal
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