Download 1 WEATHER PREDICTION EXPERT SYSTEM

WEATHER PREDICTION EXPERT SYSTEM APPROACHES
(Ceng-568 Literature Survey)
Bulent KISKAC and Harun YARDIMCI
Computer Engineering
Middle East Technical University
Ankara,TURKEY
June, 2004
ABSTRACT
Weather prediction approaches are challenged by complex weather phenomena
with limited observations and past data. Weather phenomena have many parameters that
are impossible to enumerate and measure. Short-term local weather prediction accuracy
and timeliness can save million dollars while a large-scale prediction can save billion
dollars and lives. Fuzzy Case Based approach and Artificial Neural Network approach
are practical and successful implementations for local area short-term weather forecast.
Increasing development on communication systems enabled weather forecast expert
systems to integrate and share resources and thus hybrid system has emerged. Even
though these improvements on weather forecast, these expert systems can not be fully
reliable since weather forecast is chaotic problem.
1. INTRODUCTION
Weather is one of the most effective environmental constraints in every phase of
our lives. We are subject to adjusting ourselves with respect to weather condition from
our dressing habits to strategic organizational planning activities, since the adverse
weather conditions may cause a considerable damage on our lives and properties. We
need to be on alert to these adverse weather conditions by taking some precautions and
using prediction mechanisms for early warning of hazardous weather phenomena.
Weather prediction is an indispensable requirement for all of us. There is a
general and increasing interest on weather information, since every day we habitually
give an ear to weather forecast news for local and large-scale long-term or short-term
weather predictions. Leading weather research institutions and companies have been
developing weather prediction systems capable of detecting, predicting and forecasting
weather phenomena and hazards by utilizing state-of-the-science technologies. Thus
weather prediction utilizations fields and prediction accuracy increases monotonically by
the time.
In this survey we try to give readers an overview about weather prediction
phenomena, expert systems approaches, main domain specific problems, and solution
methodologies. We made this research in parallel with our KE project about a local
airbase short-term weather prediction implementation with Case Base Reasoning KNN
algorithm, Fuzzy logic and Artificial Neural Network implementation and their
performance comparisons. That is why our survey mainly focuses on local and short-term
predictions when covering all literatures about weather prediction expert systems.
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1.1 Weather Prediction Problem Characteristics?
Weather prediction is a complicated procedure that includes multiple specialized
fields of expertise. “There are only two methods to predict weather: the empirical
approach and the dynamical approach” (Lorenz 19) [1]. Lorenz thus separated weather
forecasting methodologies into two main branches in terms of numerical modeling and
scientific processing (AI) of meteorological data.
Hansen[2] explains this classification as follows; The empirical approach is based
upon the happenings of comparable cases (i.e., similar weather situations). The empirical
approach is useful for predicting local-scale weather if recorded cases are plentiful (e.g.,
cloud ceiling and visibility in a few square kilometers around an airport). The dynamical
approach is based upon equations of the atmosphere and is commonly referred to as
computer modeling. The dynamical approach is only useful for modeling large-scale
weather phenomena (e.g., general wind direction over a few thousand square kilometers).
The dynamical approach is a NWP method that takes inputs readings from
weather stations, weather buoys, satellite images, atmospheric probes and other sources
and computes solar energy effects and global rotational effects, latitude effects, ocean
currents effects, landmasses effects and produces global weather predictions. NWP
models build these computation results as initial conditions to systems of equations that
describe the atmosphere. The models are run statically and produce forecasts on a six
hourly basis that serves as the foundation of all forecasts. Output from these models is
useful for large scale and longer-term forecasts. However, the complete description of the
atmosphere in this form is far beyond current capability that requires more
improvements, more measurement, and more parameters. Since the accuracy of the
models is inherently dependent upon the initial conditions that are inherently incomplete,
these systems are not able to produces results that satisfies everybody in local and shortterm cases.
The empirical approach mostly depends on past meteorological data assuming
that initially similar conditioned weather conditions shall go in parallel for some time
enough to make a short range weather prediction. Huschke[3] defines a “persistence
forecast” as “a forecast that the future weather conditions will be the same as the present
conditions.” This approach is an analogical method of weather forecasting that bases
predictions for the present case on the outcomes of similar past cases. Utilizing past
meteorological data is not as easy as it seems, since there are many problems to be
handled, like deciding similar cases in continuous weather parameters. There are many
different solution trials like using Case Base Reasoning, Artificial Neural Network,
Decision Trees and Rule Production System etc.
1.2 Short term and long term forecast
Lately expert systems using artificial intelligence (AI) techniques have been used
to forecast visibility, marine fog, precipitation, severe weather and other climatological
conditions [e.g., 4,5,6,7]. Expert systems on smaller scale model have many of the
characteristic advantages that typically arise when AI techniques are applied. First they
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are guided and taught by human domain experts so they become more adaptive with this
heuristic guidance for using knowledge acquisition techniques. Secondly they can cope
with uncertainty, incompleteness and recency. Thirdly they are not computationally
intensive as much as NWP systems.
Large Scale weather prediction information can easily be accessible from weather
report services for a big region or a city. They report like tomorrow heavy rain expected
in this city and visibility will be 10 km. and so on. However, large-scale static forecasts
are not sufficiently stable on a small scale where local effects and recency become
significant or predominant.
1.3 Forecast Types
Weather forecast expert systems can be categorized as terminal aerodrome
forecasts (TAFs), public forecasts and marine forecasts in terms of field of interest and
scale. In addition to these, many specialized forecasts are produced for specific purposes
(e.g. weather pollution forecasting, agricultural predictions)
TAFs are required to be most precise both in terms of measurable weather
conditions and in terms of timing. TAF forecasts of the height of low cloud ceiling are
expected to be accurate to within 100 feet; forecasts of the horizontal visibility on the
ground, when there is dense obstruction to visibility, such as fog or snow, are expected to
be accurate to within 400 meters Vislocky [8]; and forecasts of the time of change from
one flying category to another are expected to be accurate to within one hour Wilson
[10].
On the other hand public and marine forecasts can be less precise. In public
forecasts, for example, it may be sufficient to predict “cloudiness this morning,” and in
marine forecasts, it may be sufficient to predict “fog patches forming this afternoon.”
1.4 Importance of TAF
Airports organize air traffic mainly based on local weather condition that has agile
parameters that may change in a short time. These parameters such as horizontal visibility
and vertical visibility (ceiling), fogginess, precipitation level etc. can be very serious
threat for flight safety and cost. When ceiling and visibility at a busy airport are low, in
order to maximize safety, the rate of planes landing is reduced. When ceiling and
visibility at a destination airport are forecast to be low at a flight’s scheduled arrival time,
its departure may be delayed in order to minimize traffic congestion and related costs.
For Airport weather forecast timeliness and correctness are the main issues.
An examination of the causes and effects of flight delays at the three main airports
serving New York City concluded that a correctly forecast timing of a ceiling and
visibility event (i.e., a significant change) could be expected to result in a savings of
approximately $480,000 per event at La Guardia Airport [10].
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2. WEATHER PREDICTION EXPERT SYSTEMS
From now on, since NWP approaches are another research subject. In this survey,
we will focus on expert systems with empirical approaches that use past meteorological
data and AI algorithms. We will explain their relative success and their constraining
factors.
Our research sources are mainly related with experimental papers, our own flight
alert system implementation (FA-1) has many clues, interesting results, and illuminating
information for readers.
2.1 Case Base Reasoner Expert Systems
Case-based reasoning has been used to solve problems in diverse areas including
decision support, help desk support, product cataloguing and maintenance support [11,
12]. In order to solve a current problem a case-based reasoner requires a library of past
problems and the solutions that were used to solve the past problems. The case-based
reasoner works by using a measure of similarity to retrieve past problems that are most
similar to the current problem. The reasoner then combines and adapts the solutions to
the most similar past problems to generate a proposed best solution to the current
problem. Clearly the designs of the similarity metric and the adaptation algorithm are
crucial to the functionality of any case-based reasoner.
There are many similarity metrics that can be used in case-based reasoners for
retrieving similar past case. Euclidean norms and Hamming distances are the most
common; rule-based metrics have been discussed [13]; some metrics use domain
knowledge [14]; similarity measures have been adapted to use fuzzy set based formalisms
[15] and others use hybrid fuzzy measures [16].
FA-1 system uses Euclidean norms to retrieve past similar case. First it calculates
relative distances of past data rows with respect to our current case attributes, after
finding relative distances we select K nearest rows. Past data is stored consecutively in a
relational database and in order of hourly interval. Easiest way is actually to give K=1 so
we get nearest neighbor quickly but this is not proper because we have to take probability
distribution of cases into account, maybe this 1 nearest neighbor has noisy and
incomplete data. For this reason we select K=16 and calculated every attribute value by
taking a weighted average. You can clearly see how to KNN work for continuous values
in Figure-1 This result most probably does not exist in past meteorology database but
again we calculate 1 nearest neighbor this second process ensure us that we are not most
probably affected by noisy data and abnormal cases. After finding most similar past
record we retrieve next record as a solution for hourly weather prediction. We assume
every attribute has equal weight with respect to each other. For example Temperature
attribute has (min= –20, max= +50) dynamic range when Ceiling attribute has (min=0,
max=100000feet) dynamic range. Euclidean distance equation naturally gives more
weight to Ceiling attribute. We need to stretch attributes to same dynamic range in
accordance with their probability distribution. This stretching method requires some
statistical approaches. FA-1 is now limited to correctness of this stretching. So we
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conclude that even such a simple implementation is not easy to handle that require
domain expertise and guidance.
Figure 1
One of surveyed papers claims that they used fuzzy set based formalisms and
achieved great success [2]. Actually this second method seems very promising because
they convert all database attributes from continuous values to discrete values and in every
fuzzy attribute values set, each member has differing weight; for example rain condition
and drizzle condition are very near to each other but snow has totally different event has
totally different conditions.
Fuzzy logic imparts to case-based reasoning the perceptiveness and case
discriminating ability of a domain expert. The fuzzy KNN technique retrieves similar
cases by emulating a domain expert who understands and interprets similar cases. The
main contribution of fuzzy logic to case-based reasoning (CBR) is that it enables us to
use common words to directly acquire domain knowledge about feature salience. This
knowledge enables us to retrieve a few most similar cases from a large temporal
database, which in turn helps us to avoid the problems of case adaptation and case
authoring.
Case Base Reasoning expert systems require very large historical weather
observation records, these records must be noise-free and consistent and well organized.
On the other hand local weather condition may change by the passing years because of
other environmental effects. For example a newly constructed damn, or a destructed
forest may change local effects; so yearly rainfall amount will change depending on these
incidents. Now historical data will be misleading anymore. That is why there must be a
forgetting mechanism for historical meteorological data. Last 30-40 years of
meteorological data observation will be enough to produce satisfactory results [2].
Forgetting mechanisms [18] are used in many case based systems to help control the
amount of memory used by the case library. Forgetting can be achieved by the use of
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recency in the similarity algorithm and weighting each passing years decreasingly or
removing older cases up to a point without adversely affecting the accuracy of the
forecasts produced by the system.
During the problem-solving process, a number of similar solutions are retrieved
not one of which is completely valid. The retrieved solutions need to be adapted to arrive
at a valid solution to the current problem. Adaptation mechanisms are often user-driven
and can be rule-based or rely on generalization and refinement heuristics [19]. Adaptation
mechanisms have been categorized as compositional or transformational [20].
Compositional adaptation occurs when knowledge is used to combine portions of
approximate solutions to achieve the desired solution. Transformational adaptation occurs
when more comprehensive knowledge needs to be used to add extra components to the
retrieved solutions to achieve a satisfactory result.
Hansen[2] has built an airport TAF Expert System by using fuzzy K Nearest
Neighbor Case Based Approach, he performed fuzzy logic by setting attribute values to
different sets with a different membership rates. Domain experts can easily change these
weights of attribute values in fuzzy set memberships. He does not open his detailed work
but he has broadcasted experimental results, he claims that his similarity measure
approach has given the best results in comparison to other approaches. The quality of
TAFs is determined by the accuracy of the forecast weather elements and the timeliness
of issue.
Hansen thought that combination of the attributes is so crucial to obtain high
accuracy in short-term weather prediction. Even though, he tested varying attributes set in
Figure-2. Hansen’s [2] test helps selecting related attributes pairs and combinations. We
can find related and dependent attributes by different test over attributes pairs and
combinations.
Figure 2
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Combinations of weather attributes considered are;
• cig & vis – cloud ceiling height and visibility alone
• pres + cig & vis – mean sea level pressure and cloud ceiling height and
visibility
• pcpn + cig & vis – precipitation type and cloud ceiling height and
visibility
• temps + cig & vis – dry bulb temperature and dew point temperature and
cloud ceiling height and visibility
• time + cig & vis – time of day and time of year and cloud ceiling height
and visibility
• wind + cig & vis – wind speed and direction and cloud ceiling height and
visibility
• All of the aforementioned attributes.
He also tested effect of varying case base in Figure-3. As the history goes back
there is a small increase for reliability but after 4 year increase slows down. This kind of
test can help us to decide our database size. At the first year reliability start from %90
percents succeeded. So we should not much be concerned about our small database size.
Figure 3
He tested varying k, the number of nearest neighbors that are used as the basis of
predictions (K = 1,2, 4, 8, ... , 256) and finds that maximum accuracy is achieved with K
= 16 (For that reason we got K=16 to achieve maximum reliability in our FA-1 system).
Assuming that fuzzy KNN similarity metric is effective, if k is too small, prediction result
accuracy should suffer from sample size being too small (i.e., not representative), and if k
is too large, prediction result accuracy should taper off because of the inclusion of an
increasing number of decreasingly similar cases. Accordingly, Figure-4 shows that the
fuzzy KNN similarity metric is effective.
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Figure 4
Hansen[2] start using fuzzy logic in large scale from beginning to end of
implementation. He gets rid of continuous attributes by converting them into fuzzy
membership set with different weights. Solution sets members are also categorized and
classified. For example we have retrieved 10 nearest neighbors. We need to find visibility
attribute of current case from 10 visibility attribute values of nearest neighbors.
Neighbor 1
Neighbor 2
Neighbor 3
Neighbor 4
Neighbor 5
Neighbor 6
Neighbor 7
Neighbor 8
Neighbor 9
Neighbor 10
Table 1
Neighbor similarity based on
Neighbor
other attributes
Visibilities
slightly near
Visibility=30
near
Visibility=26
very near
Visibility=24
slightly near
Visibility=31
slightly near
Visibility=14
near
Visibility=29
very near
Visibility=23
very near
Visibility=22
slightly near
Visibility=10
very near
Visibility=25
Weight
.25
.50
1
.25
.25
.50
1
1
.25
1
6
7.5
13
24
7.6
3.5
14.5
23
22
2.5
25
140/6~=23
Table 1 show a simple visibility calculation on 10 retrieved neighbors and we
found 23 km which is neighbor 7.
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An example attribute values similarity table is given for precipitation type on
Figure-5.
Figure 5
2.2 Weather Prediction Using Artificial Neural Network
For an expert system implementation we surveyed a paper of Manan[23]. This
project implemented an expert system that is capable to learn the pattern of rainfalls in
order to produce a precise forecasting result. He used the multiplayer perceptron with
error back-propagation algorithm to provide prediction of rainfall. The ANN technique is
applied to the hourly local weather data for a specific area. His main motivation for
selecting ANN algorithm comes from the analogy that climate and rainfall are highly
non-linear phenomena and the multilayer perceptron neural network has been designed to
function well in such non-linear phenomena.
A feed-forward multilayer neural network consists of an input layer and output
layer. There are also one or more hidden layers in between the input and the output layer
with some number of neurons on each. 9 distinct weather features are selected time of the
day, pressure, dry bulb temperature, wet bulb temperature, dew point, wind speed, wind
direction, cloud cover and whether it rained in the past hour. Since our target is to
forecast rainfall, initial condition is the rest of features. They are fed to each input
neurons while rainfall attribute is fed to output neuron. The network learns the
relationship between the input-output data in the training set via network training in
which the weights are modified by presenting input-output patterns of the training set
until a prescribed error criterion is fulfilled. This simple implementation experiments can
give us practical results. Back propagation algorithm adjusts weights of links between
neurons in order to decrease some of squared errors between expected outputs and
observed output values until a specified termination condition is met. This termination
condition can be an error threshold or a number of loop count (epoch), Learning rate and
momentum constants are used when tuning weights in a step logic. All of these
parameters and constants should be customized to produce best result from constructed
neural network.
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Actually this implementation seems very easy with respect to our FA-1 expert
system. We need to forecast all multiple features at the same time. For a selected hourly
weather observation, relevant parameters are fed to input neurons while next hour
parameters are fed to output neurons. Topology of neural (number of hidden units and
layers) network may seriously affect performance.
In Figure-6 results of learning and testing in different architecture networks and
parameters settings Manan [23]. This experiment enables us to make a generalization
about ANN performance for weather prediction. These different network configurations
produce approximately the same result. We can conclude that ANN is successful at most
%80, on the other hand there may be some reasons affecting performance, maybe training
data was not enough to represent all behavior of weather phenomena.
Figure 6
2.3 Hybrid Weather Prediction
For every special purpose we are forced to implement different projects and to
utilize different methods. In this way it is impossible to produce this specific
implementation for everywhere. Weather prediction systems must be integrated.
Nowadays, hybrid systems swiftly spread all around the world. They use multiple
methods and sensors to feed each other. In this way even our simple weather forecast
implementations will be able to receive input readings from satellites to weather doppler
radars or even other expert system forecasts will guide our expert systems. This newly
developing approach will certainly increase weather forecast accuracy and timeliness
Up to now we mentioned about empirical methods based on occurrence of analog
or similar weather situations, whereas dynamical methods can guide our empirical
methods and vice versa. In practice, hybrid methods use dynamic models and empirical
observations. In addition to using statistical methods that infer estimated expected
distributions under specified conditions. Hybrid systems cannot go further than being a
decision support system because problem area is inherently complicated and not
deterministic.
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•
•
•
•
Here are the some of the Hybrid Forecast Decision Support Systems;
Data fusion: intelligent integration of output of various models, observational
data, and forecaster input using fuzzy logic [22]
Data mining: C5.0 pattern recognition software for generating decision trees
based on data mining, freeware by Ross Quinlan (http://www.rulequest.com), like
CART
Incorporate AutoNowcast of weather radar in 2004-2005
(http://www.rap.ucar.edu/projects/nowcast)
Incorporate satellite image cloud-type classification algorithms [23]
Hybrid systems designs must obey some standardization rules for easy
integration;
• Generic: no-name, conceptual design that could link and integrate the most useful
elements of WIND, AVISA, MultiAlert, SCRIBE, FPA, URP, and so on in
evolving WSP application.
• Modular: shows where distinct sub-tools / agents can be developed. Working in
this way, individual developers could work on isolated sub-problems and
anticipate how to plug their results into a larger shared system. As technology
inevitably improves, improved modules can be easily installed and quickly
implemented.
• User-centered: forecast decision support systems from forecaster's point of view,
designed to increase situational awareness.
• Hybrid: combines complementary sources of knowledge, forecasters and AI, to
increase the quality of input data and output information. Intelligent integration of
data, information, and model output, and use of adaptive forecasting strategies are
intrinsic in this design.
3. COMPARISON of MENTIONED APPROACHES
Case Base Reasoning systems can easily use fuzzy logic while other methods
such as ANN and Decision Trees etc. it is hard to manage. Case Base Reasoning
experiment results outperform ANN experiment results. Thus we can conclude that Case
Base Reasoning is inherently associated with weather phenomena. But none of the
advantages are free for Case Base Reasoning. As we said Case Base Reasoning uses
stored past data, and retrieve the solution from this database with respect to present
situation. It has to find all the Euclidian distance for every instance in the database. It
needs much more computation power and large memory. From ANN point of view, the
system trained at once, and the weight values are stored in the simple database table.
When the new current instance comes, ANN easily computes the output values.
ANN can be used to simple predictions like whether rain is expected and clear
weather expected but not multiple parameter result.
There are many other test results for decision trees and rule based productions
system and statistical forecast systems. Our implementation and handling errors made
these approaches less successful. This methods are hard to model and construct that is
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why they are not much preferred when such a successful Case Base Reasoning approach
exist.
CONCLUSION
Weather prediction is inherently complex process, so it impossible to wait %100
accurate forecast results since we cannot measure all factors that may be local and global
scales. Weather Prediction systems are more likely decision support system than expert
systems because they need guidance and weather predictions must be evaluated by
human interference.
Hybrid systems are very promising for integration of current expert systems on
large scale. On Internet, there are many web weather report and forecast service systems
that enable our implementations to fetch some weather information. Since there are many
weather related data needs to be processed, hybrid system can reduce intensive forecast
computations. In stead of computing some detailed information, we can easily pick up
knowledge about a satellite post-processing reports and comments
Fuzzy k nearest neighbor in Case Base Reasoning is the most promising approach
with successful test results. All reviewed test results are limited to past meteorological
data that will represent all possible weather condition distributions. Data collecting and
acquisition are initial and one of the most critical parts of expert systems computations.
Automated data collecting systems must be available rather than using human manual
inputs. Local data collection is very important if we want to make very precise
forecasting.
Even though these fast developments, meteorologists go without artificial
intelligence benefits, they still use classical methods. There will be many fields that use
weather prediction for specific purposes. So we are always bound to meteorological
reports and predictions.
Abbreviations:
AI
: Artificial Intelligence
CBR : case-based reasoning
DBMS : database management systems
DSS : decision support system
FOH : Frequency of Hits
ES
: Expert Systems
FA-1 : Flight Alert Systems [Bulent KISKAC , Harun YARDIMCI]
FAR : False Alarm Ratio
KE
: Knowledge Engineering
KNN : K nearest neighbor
MOS : model output statistics (NWP + climatology + statistics)
NWP : Numeric Weather Prediction
PC
: Persistence Forecast
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POD
TAF
VFR
: Probability of Detection
: Terminal Aerodrome Forecast
: Visual Flight Rules
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