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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
203
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Citations (Scopus)
283
Downloads (Pure)
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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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Keyphrases
Deep Learning Methods
100%
Weather Forecasting
100%
Weather Forecasting Model
100%
Long Short-term Memory
75%
Temporal Convolutional Network
75%
Machine Learning Approach
50%
Weather Research
50%
Numerical Weather Prediction Model
50%
Dynamic Ensemble
50%
Statistical Forecasting
50%
Forecasting Approach
50%
Ensemble Methods
50%
Classical Machine Learning
50%
Neural Network
25%
Modeling Approach
25%
Deep Learning Network
25%
Forecasting Model
25%
Random Forest
25%
Weather Conditions
25%
Time Series Data
25%
Network Model
25%
Complex Weather
25%
Deep Model
25%
Mathematical Equations
25%
Weather Information
25%
Weather Prediction
25%
Lightweight Data
25%
Weather Parameters
25%
Regression Forest
25%
Arbitrage
25%
Vector Autoregression
25%
Multiple-input Single-output
25%
Regression Error
25%
Vector Error Correction Model
25%
Multi-input multi-output
25%
Least Squares Support Vector Regression (LSSVR)
25%
Temporal Modeling
25%
Autoregressive Integrated Moving Average (ARIMA)
25%
Convolutional Layer
25%
On-state Current
25%
Lightweight Model
25%
Engineering
Deep Learning
100%
Long Short-Term Memory
100%
Learning Approach
66%
Weather Research and Forecasting
66%
Numerical Weather Prediction Model
66%
Statistical Forecasting
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%
Computer Science
Deep Learning
100%
Long Short-Term Memory Network
100%
Temporal Convolutional Network
100%
Machine Learning Approach
66%
Prediction Model
66%
Ensemble Method
66%
Neural Network
33%
Random Decision Forest
33%
Weather Condition
33%
Time Series Data
33%
Mathematical Equation
33%
Support Vector Regression
33%
Temporal Modeling
33%
Moving Average
33%
Error Correction
33%
Network Layer
33%
Mathematics
Forecasting Model
100%
Deep Learning
100%
Periodic Time
33%
Mathematical Equation
33%
Time Series Data
33%
Neural Network
33%
Autoregressive Integrated Moving Average
33%
Support Vector Machine
33%
Statistical Approach
33%
Vector Error
33%
Vector Autoregression
33%
Modeling Approach
33%
Network Model
33%
Regression Error
33%
Error Correction
33%
Earth and Planetary Sciences
Weather Forecasting
100%
Machine Learning
50%
State of the Art
25%
Time Series
25%
Weather Condition
25%
Numerical Weather Forecasting
25%
Error Correction
25%
Vector Autoregression
25%