Predicting Frost Using
Artificial Neural Network
AUTHORS
Mr. Abhishek Jain
Ms. Ramyaa
Dr. Ronald W. McClendon
Dr. Gerrit Hoogenboom
Presenters
Mr. Abhishek Jain
Ms. Ramyaa
Overview
Introduction
Prediction Approach
Objectives
Methodology
Results
Future Work
Classification Approach
Objectives
Methodology
Results
Future Work
Questions
Introduction
Need
Georgia Automated
Environmental
Monitoring Network.
Why ANN?
Forecasting
–
Two
formats
Prediction
Classification
Predicting Approach
The overall goal of this part of the
research is to develop ANNs that
would predict temperatures for
subsequent 12 hours beginning with
1 hour and at hourly interval .
Objectives
Determine the most important input
parameters.
Determine duration of prior data needed
Develop the best ANN architecture once the
prior objectives have been met.
Determine whether input data of 15 minute
resolution or hourly resolution yields more
accurate forecasts.
Determine whether ANNs developed for a
specific location can be used to make
forecasts at other locations.
Methodology
Organizing Data
Data used from Fort Valley
Date from January to April
Δ
values;
Δ
= present value
–
past value
Input Parameters
-
Temperature, Relative Humidity,
Rainfall, Solar Activity and Wind
Speed and their prior and
Δ
values, Time of Day
Output Parameters
–
Subsequent Temperatures
Prior Data
–
1 hour prior to 6 hour Prior
Production Set
–
Data from years 2001 and 2002
Training and Testing Set
–
Prior to 2001.
Error Measure
–
Mean Absolute Error.
Methodology
Determining which inputs are most important
Prior data is kept constant.
Start with Temperature terms only and then add
one set of parameter terms, one at a time.
Determine most important, and then keep this
constant with
temperature and experiment with
remaining terms adding one at a time and the
process continues.
Determining best prior data configuration
Input parameters kept constant.
Determining best of 15 minute resolution or
hourly resolution data
Robustness of the Models.
Results
Table 1: Impact of various input parameters on the accuracy of the predictions
Temperature
Relative
Humidity
Wind
Solar
Radiation
Rainfall
MAE*
9 Nodes
9 Nodes
9 Nodes
9 Nodes
9 Nodes
(In degree C)
X
1.41
X
X
1.19
X
X
1.34
X
X
1.35
X
X
1.44
X
X
X
1.15
X
X
X
1.17
X
X
X
1.21
X
X
X
X
1.12
X
X
X
X
1.16
*
-
The Mean Absolute Error (MAE) was calculated for only temperatures below 5
o
C in the production set
X
–
Denotes that which input parameters were used. For any given parameter there are 4 prior data values (corresponding to 4 hou
rs of prior data corresponding to 4 input nodes),
4
values (corresponding to another 4 input nodes) and current value (corresponding
to one more input nodes)
Results
Table 2: Impact of prior data on the accuracy of the predictions
Prior Data
(Hour)
Prediction Duration
(Hour)
MAE
(C)
2
1
0.59
4
1
0.64
6
1
0.66
2
12
2.49
4
12
2.48
6
12
2.47
Future Work
15 minute vs Hourly ?
Different Locations?
Classification Approach
The overall goal of this part of the
research is to develop ANNs that
would classify whether temperatures
for subsequent ‘n’ hours beginning
with 1 hour and at hourly interval
would be freeze temperatures or not.
Objectives
The objectives of the previous
approach apply to this one too
–
though the outcome may differ
–
the
set of inputs needed to predict the
temperature may not be the best
ones to predict freeze
–
though we
expect this to be unlikely.
Methodology
The process of meeting the objectives
and getting “the best” network
involves evaluating a network
–
we
are currently working on a
formalization of the evaluation of
different networks.
The evaluation of a network is not
straight forward in this approach
Evaluation of the network
The problem is to classify the current
weather data as corresponding to one that
precedes a “freeze event” or not.
The freeze events are very scarce and are
not evenly distributed.
Failing to predict a freeze event is worse
than doing a false prediction of a freeze
event.
Importance of predicting correctly or
incorrectly depends on the current
temperature.
Evaluation of the network
Importance of missing a freeze event
depends on the length of time interval from
now to the freeze event.
Evaluation of output should give different
weights to a missed prediction when the
network is sure of its wrong output and one
when the network is in doubt
Evaluation should give different weights to
a missed prediction when the temperature
just reached 0
º and rose again immediately
and a freeze event which reached lower
temperatures and lasted longer.
Results
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1.
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Future Work
Different Locations
This is the real challenge as different
locations typically have different
distribution of freeze events
–
and it is
important that the network is a general
one and is not sensitive to the number of
freeze events.
Questions??
Thank You
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