1. Predicting Frost Using Artificial Neural Network
AUTHORS
Mr. Abhishek Jain
Ms. Ramyaa
Dr. Ronald W. McClendon
Dr. Gerrit Hoogenboom
Presenters
Good Afternoon Ladies and Gentlemen,
My name is Abhishek Jain and this is my research partner Ms. Ramyaa. Together we hope to bring you upto speed on our research which deals with predicting the formation of frost by using Artificial Neural Networks. The research is being guided by Dr. Ronald McClendon and Dr. Gerrit Hoogenboom.
(25 seconds)

2. Overview Introduction Prediction Approach Classification Approach
Objectives
Methodology
Results
Future Work
Classification Approach
Questions
Before we begin, I would like to present a brief over view of the presentation. I shall begin by introducing you to the project its background and the need for it. Following this the presentation is divided into two parts two reflect the approaches that have been explored to solve the problem, the first part would elaborate on the prediction approach and the second part would concern itself with the classification approach.

3. Introduction Need Georgia Automated Environmental Monitoring Network.
Why ANN?
Forecasting – Two formats
Prediction
Classification
Need – By Farmers – The NWS does not provide data for agriculture applications. Besides they only collect from urban areas, which is of limited use to farmers.
In the spring of 2002 a large area in blueberry and peach production was destroyed in South Georgia due to unusually severe and late frost.
AEMN – As a first response to this need the AEMN was setup by UGA. It is a network of 50 weather stations that collect meteorological data from the remote areas of GA. Information is disseminated through a website. But it lacks a forecasting tool which would be useful.
Data – Data is collected by these weather stations every second and the averages or totals depending on the variable are stored every 15 minutes and stored in a data logger. The weather stations collect data for wind speed, rainfall, solar radiation, temperature, relative humidity, soil temps at various depths etc.
Why ANN – Because they can easily capture the complex and non-linear relationship between the variables in a meteorological arena.
I will talk about data where it comes from, what is included in the data, historical data, locations, why this is needed, who is biggest beneficiary, prior work done on the prediction part only,

4. 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 .

5. 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.

6. 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.
Organizing Data – Write a program to convert raw data to Neuroshell
Data from the months of January to April.
Prior data from -1 to -6.
How I propose to meet all the objectives, Error Measure etc. Might be more than one slide>

7. 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.

8. Results
Table 1: Impact of various input parameters on the accuracy of the predictions
Temperature
Relative
Humidity
Wind
Solar
Radiation
Rainfall
MAE*
9 Nodes
(In degree C) X
1.41
1.19
1.34
1.35
1.44
1.15
1.17
1.21
1.12
1.16
Which inputs were useful – Table
How much prior data was needed.
<Graph of MAE vs Time of Prediction>
* - The Mean Absolute Error (MAE) was calculated for only temperatures below 5oC 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 hours 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)

9. Results
Table 2: Impact of prior data on the accuracy of the predictions
Prior Data
(Hour)
Prediction Duration
MAE
(C) 2 1
0.59 4
0.64 6
0.66 12
2.49
2.48
2.47

24. Future Work
15 minute vs Hourly ?
Different Locations?

25. 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.

26. 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.

27. 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

28. 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.

29. 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 0º and rose again immediately and a freeze event which reached lower temperatures and lasted longer.

30. Results
0.0– – –

31. 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.

32. Questions??
Thank You