Spatio-temporal prediction of snow cover in the Black Forest mountain range using remote sensing and a recurrent neural network

2010 ◽  
Vol 30 (15) ◽  
pp. 2330-2341 ◽  
Author(s):  
Tobias Sauter ◽  
Björn Weitzenkamp ◽  
Christoph Schneider
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Snow Cover ◽  
Recurrent Neural Network ◽  
Mountain Range ◽  
Black Forest ◽  
Temporal Prediction ◽  
Spatio Temporal ◽  
Forest Mountain

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.

ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network

2020 ◽  
Vol 106 ◽  
pp. 101856 ◽  
Author(s):  
Jing Zhang ◽  
Aiping Liu ◽  
Min Gao ◽  
Xiang Chen ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Temporal Attention ◽  
Arrhythmia Detection ◽  
Spatio Temporal

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 Convolutional LSTM Architecture for Precipitation Nowcasting Using Satellite Data

2021 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Carlos Javier Gamboa-Villafruela ◽  
José Carlos Fernández-Alvarez ◽  
Maykel Márquez-Mijares ◽  
Albenis Pérez-Alarcón ◽  
Alfo José Batista-Leyva
Keyword(s):  
Neural Network ◽  
Satellite Data ◽  
Training Data ◽  
Time Interval ◽  
Precipitation Data ◽  
Short Term ◽  
Data Set ◽  
Temporal Prediction ◽  
Spatio Temporal ◽  
Convolutional Lstm

The short-term prediction of precipitation is a difficult spatio-temporal task due to the non-uniform characterization of meteorological structures over time. Currently, neural networks such as convolutional LSTM have shown ability for the spatio-temporal prediction of complex problems. In this research, we propose an LSTM convolutional neural network (CNN-LSTM) architecture for immediate prediction of various short-term precipitation events using satellite data. The CNN-LSTM is trained with NASA Global Precipitation Measurement (GPM) precipitation data sets, each at 30-min intervals. The trained neural network model is used to predict the sixteenth precipitation data of the corresponding fifteen precipitation sequence and up to a time interval of 180 min. The results show that the increase in the number of layers, as well as in the amount of data in the training data set, improves the quality of the forecast.

scholarly journals Spatio-Temporal Check-in Time Prediction with Recurrent Neural Network based Survival Analysis

2018 ◽  
Author(s):  
Guolei Yang ◽  
Ying Cai ◽  
Chandan K Reddy
Keyword(s):  
Neural Network ◽  
Survival Analysis ◽  
Recurrent Neural Network ◽  
Censored Regression ◽  
Event Time ◽  
Time Prediction ◽  
Spatio Temporal ◽  
Latent Representations ◽  
Real World Datasets ◽  
Prediction Techniques

We introduce a novel check-in time prediction problem. The goal is to predict the time a user will check-in to a given location. We formulate check-in prediction as a survival analysis problem and propose a Recurrent-Censored Regression (RCR) model. We address the key challenge of check-in data scarcity, which is due to the uneven distribution of check-ins among users/locations. Our idea is to enrich the check-in data with potential visitors, i.e., users who have not visited the location before but are likely to do so. RCR uses recurrent neural network to learn latent representations from historical check-ins of both actual and potential visitors, which is then incorporated with censored regression to make predictions. Experiments show RCR outperforms state-of-the-art event time prediction techniques on real-world datasets.

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