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Deep Learning Based Effective Fine-grained Weather Forecasting Model
PRADEEP RUWAN PADMASIRI GALBOKKA HEWAGE
, MARCELLO TROVATI
,
ELLA PEREIRA
,
ARDHENDU BEHERA
Computer Science
Faculty of Arts & Sciences
Centre for Intelligent Visual Computing Research
Data and Complex Systems Research Centre
Data Science STEM Research Centre
Research output
:
Contribution to journal
›
Article (journal)
›
peer-review
224
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Citations (Scopus)
397
Downloads (Pure)
Overview
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Dive into the research topics of 'Deep Learning Based Effective Fine-grained Weather Forecasting Model'. Together they form a unique fingerprint.
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Engineering
Long Short-Term Memory
100%
Deep Learning Method
100%
Long Short-Term Memory Network
100%
Learning Approach
66%
Weather Research and Forecasting
66%
Numerical Weather Prediction Model
66%
Statistical Forecasting
66%
Learning System
66%
Periodic Time
33%
Data Series
33%
Mathematical Equation
33%
Autoregression
33%
Layer Network
33%
Input Multi
33%
Random Forest
33%
Error Correction
33%
Multi-Input Multi-Output
33%
Moving Average
33%
Network Model
33%
Single Output
33%
Support Vector Machine
33%
Earth and Planetary Sciences
Weather Condition
100%
Weather Forecasting
100%
Long Short-Term Memory Network
50%
Long Short-Term Memory
50%
Machine Learning
33%
State of the Art
16%
Time Series
16%
Numerical Weather Forecasting
16%
Error Correction
16%
Vector Autoregression
16%
Support Vector Machine
16%
Computer Science
Deep Learning Method
100%
Weather Condition
100%
Long Short-Term Memory Network
30%
Temporal Convolutional Network
30%
Machine Learning Approach
20%
Prediction Model
20%
Ensemble Method
20%
Neural Network
10%
Random Decision Forest
10%
Time Series Data
10%
Mathematical Equation
10%
Support Vector Regression
10%
Temporal Modeling
10%
Moving Average
10%
Error Correction
10%
Network Layer
10%
Mathematics
Long Short-Term Memory Network
100%
Deep Learning Method
100%
ARMA Model
33%
Vector Autoregressive Model
33%
Keyphrases
Multi-output
25%
Temporal Modeling
25%
Convolutional Network Layers
25%
Multi-input Single-output
25%