Download hgh - (ACIT) Center

Mina Moradi
PhD student, ECE, NCAT
Supervisor: Dr. A. Homaifar
July, 2015
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Outline
 Introduction and motivation
 Data representation
 Proposed method
 Simulation and results
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Motivation
• Hurricanes are a vast mass of
clouds that form in the tropics, bring
high winds, heavy rains, and
dangerous tides, and release huge
amounts of energy
• During the last thirty hurricanes of
the 20th century, 587 U.S. residents
died.
• Predicting the trajectory of ongoing
hurricane is important.
• Predicting where a hurricane will
located.
• Warning for an area that is likely to
experience a hurricane within 24
hours.
http://www.thewire.com/
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Atlantic Hurricanes
 The Atlantic hurricanes are tracked from year 1851. The data source
is from NOAA (National Oceanic and Atmospheric Administration),
and is publicly available
For each hurricane, the following
information is recorded every six
hours:
 Date and time
 Hurricane id
 Hurricane name
 Position in latitude and longitude
 Maximum wind speed
 Central pressure
Since, hurricanes and tropical storms are highly erratic in movement,
changing speed or direction, forecasting their trajectories are not easy.
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2013 Hurricane/Tropical Data for Atlantic
Tropical Storm ANDREA (05-08 JUN)
Storm - Max Winds: 55 Min Pres: 992
Category: TS
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Prediction Methodology
 Goal: Predict the future trajectory (m-step) of the ongoing hurricane
(for example, predict the next 6 hours, 24 hours etc).
 Data: First k observations of the ongoing hurricane (for example, two
days of data).
 Prediction: m-step ahead sequential prediction and m-step ahead
direct prediction.
Prediction of the future trajectories are performed by:
 Finding the most similar hurricanes to target hurricane.
 Constructing a nonlinear model based on information obtained from
past trajectory of similar hurricanes (past speed, past direction).
 Dynamic of the system is estimated using a partially connected
recurrent neural network while the topology and connection weights
of the network are trained through the evolutionary process of
Genetic Algorithm.
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Notation
 For hurricane i at time t :
(longitude, latitude) = (Xi(t ),Yi (t ))
 First difference:
X i1(t ) = Xi (t ) − Xi (t − 1)
Y i1 (t ) = Yi (t ) − Yi(t − 1)
 Direction (V(t ),W(t )):
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Similarity calculation
 For hurricane i and j, similarity
is defined as
 This is one interpretation of similarity, and the idea can be extended to include
lags.
 The weight of a past hurricane j is:
 We can include top 50 percent
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Hurricane Track Forecasting
 Build a relationship between its speed and acceleration.
 Numerically, the speed (with direction) is the first difference
X i1(t ) = Xi (t ) − Xi (t − 1)
Y i1 (t ) = Yi (t ) − Yi(t − 1)
acceleration is the second difference
X i2(t ) = X i1(t) − X i1(t − 1)
Y i2 (t ) = Y i1 (t ) − Y i1 ( t− 1)
X11(t )
Xn1(t )
Y11(t
Yn1(t
)
X12(t )
Y12(t )
…
)
Xn2(t )
Yn2(t )
X11(t+1 )
Model
?
Y11(t +1)
X1 (t )
X12(t +1)
Y1 (t )
Y12(t +1)
n similar hurricanes
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Artificial Neural Network (ANN)
• ANN is a data-driven approach capable of transforming the inputs into
meaningful outputs when dynamics of the system is not known
• This ability is provided by learning the connection weights and processing
units called Neurons in the network
One dimensional
time series
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Neural network for Time Series Prediction
•
•
Network topology is selected before the experiment
begins
ANN only adopt the connection weights of the fully
connected network
•
•
Connection weights are not the only aspect of ANN that need to be trained.
Topology of the network including the number of neurons, number of inputs and
outputs influence the performance of ANN
•
Neuro-Evolution i.e. evolution of neural networks, evolve the
parameters of the ANN
• The performance of ANN would be improved if an efficient
set of inputs, lag observation or memory get chosen.
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Proposed PANNET
 PANNET consists of arbitrary number of neurons (processing unit) that make
partial connection between external inputs (exogenous inputs if exist),
observation at current time, and hidden nodes.
 Hidden nodes are extra nodes that play the role of memory or internal states of
the system that are not already predetermined.
A typical PANNET
structure, includes
one external input,
three hidden, one
observation nodes
and two neurons
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Individual: A candidate
PANNET structure
with K neurons.
Crossover: A neuron in
parent 1 swapped with a
neuron in the second
parent
Parent 1
Parent 2
Child 1
Child 2
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Mutation
Mutation on number of neuron:
Mutation on number of input/output nodes:
Mutation on origin of nodes:
Mutation on connection
weight:
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-In current simulation training of the network is based on collected data from
2008-2013, simulations will be repeated when more data are provided.
-Among 98 data set, for each hurricane 48 most similar hurricanes are used
for training.
-10 initial observations of each hurricanes are used for similarity and training
process.
-In this simulation maximum 8 context nodes and 8 neurons are considered.
Tropical Storm ANDREA
(05-08 JUN 2013)
Tropical Storm BARRY
(16-21 JUN 2013)
Hurricane-3 SANDY
(21-31 OCT)
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THANKS
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