ES & GIS new

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Introduction

Expert system

• Definition

• Characteristics

• Components

• Why we need it.

GIS

• Definition

• Characteristics

• Components

• Why we need it

integration:

• Comparison between ES & GIS

How Integrated ES & GIS is ?

Previous work

Suggestions

Conclusion

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Why the integration?!!!

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An expert system is a computer that uses arepresentation of human expertise in a specialist domainin order to perform functions similar to those normallyperformed by a human expert in that domain". Goodall's (1985)

Expert systems are branch of artificial intelligence (AI) thatis the study of how to make computers do things

at which, at moment, people are better“ Elaine Rich (1983)

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Data:• measurements or records about events (prices, temperature,

etc). Data can be numerical, alphabetical, images, sounds, etc.

Information :• analyzed and organized data

such that we know itscharacteristics (a verage, range,variance, distributions,clusters, etc).

Knowledge:• information put into a specific context (e.g. distribution of oil

prices, a map of London, etc).• Who owns knowledge are called experts.

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„Knowledge engineering is the process of developing knowledge based

systems in any field, whether it be in the public or private sector, incommerce or in industry‟ (Debenham, 1988).

Knowledge engineering:normally involves five distinct steps intransferring human knowledgeinto some form ofknowledge based system(KBS) for an ES.

Knowledgeacquisition

Knowledge validation,verification, evaluation

Knowledgerepresentation

Inference engine

Explanation

“Justifier”

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Knowledgeacquisition

Knowledge evaluation,

verification, validation

Knowledgerepresentation

Inference engine

Explanation“Justifier”

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Knowledge acquisition is the process of acquiring the

knowledge from human experts or other sources (e.g.books, manuals) to solve the problem.

Knowledge acquisition

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measures the performance of the KBS

Is the process of ensuring that the outputof the system is equivalent to those ofhuman experts when given the sameinputs.

Knowledge validation

• Validate to show the rightsystem was built. Validation

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Mean how to represent knowledge in a manner that

facilitates inferencing (i.e. drawing conclusions) from

knowledge. There are Different knowledge

representation schemes: Rules, Semantic Networks

and Frames.

Knowledge representation

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It uses information and relations to derive new facts to solve problems or

predict possible outcomes. It compares each rule stored in the knowledge

base with facts contained in the database. When the IF (condition) part of

the rule matches a fact; The rule is fired and its THEN (action) part is

executed. Fired rule may change the set of facts by adding a new fact.

Inference engine

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Inference techniques:

a. Forward chaining: is a data-driven inference process; It is a

technique for gathering information and then inferring from it

whatever can be inferred. You start with facts and try to reach

conclusions. The user of the system has to give all the availabledata before the start of the inference. The inference mechanism

tries to establish the facts as they appear in the knowledge base

until the goal is established.

b. Backward chaining: You start with conclusions; It is a goal-drivenprocess and the most frequently used inference mechanism; It

tries to establish goals in the order in which they appear in the

knowledge base; The goal variable defined in the rule base for

selection of a structural.

Inference engine

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Explanation Facilities:

• A basic component of an expert system that enables

the user to query the expert system about how it

reached a particular conclusion; They can show the

sequence of the rules they applied to reach a solution,

but cannot relate accumulated, heuristic knowledge to

any deeper understanding of the problem domain.

Explanation “Justifier”

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If great costs and efforts are involved when makingthe decision

If the ES will be faster solution than the human When there are big difference between the best

solution and the worst solution

When test the data is easy to test the ES

If solving the problem is based on the knowledgerather than the common sense reasoning

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MYCIN

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“ GIS is a collection of computer hardware, software, and geographicdata for capturing, managing, analyzing, and displaying all forms ofgeographically referenced information ”

(Environmental Systems Research Institute (ESRI), 2007)

It considers with where events,activities, and things happenor exist.“ Longley et al. (2005)

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Capturing Data

Storing Data

Querying Data

Analyzing Data

Displaying Data

Outputting Data

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Capitals

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When we need to detect specific location where:

A location can be described in many different ways using, for example, place name, or coordinates

Getting other types of information about a feature

Examples :

• What kinds of patterns exist for motor vehicle accidents?

• Where do they occur and at what times?

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Integration between ES and GIS

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Concerned with the coupling of pre-existingsystems to fuse a desired set of capabilitieswith some targeted degree of usability of theintegrated system.

Pre-existing systems themselves are to betaken as not to be modified.

Involves the design of some specialistcomponents and linkage components tofacilitate coupling .

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To avoid some of the limitations anddifficulties existing in each of them

The spatial decision making can bemade more effective within suchintegrated systems.

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GIS is very suitable to well-structuredspatial problem solving, while theintegration of GIS and ES offers a best

approach to solving ill-structured spatialproblems.

The expert systems have the potential toadd intelligence to GIS tasks

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Expert systems does not adequately supportspatial decision making because it has thefollowing limitations:

1. Spatial decision making requires large volumes of spatialdata. These data mainly reside in GIS and not in ES.

2. Concentrated on symbolic reasoning and do not providegood arithmetic capabilities.

3. Lack spatial data handling capabilities such as overlay whichare unique and important to spatial analysis.

4. Do not provide facilities for spatial data representation andvisualization.

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The interwoven nature of the requestsallows the functions of two systems to behighly integrated.

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It is the ability of two or more softwarecomponents to cooperate despitedifferences in language, interface, and

execution platform

The degree of interoperability between an

expert system and a GIS will affect theability of an integrated system to model thecomplexity of the real world

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COM (Component Object Model): the basestandards to develop specifications for exchangingGIS data and functionality.

• Component Based Software Development (CBSD) thatfocuses on building large software systems by integratingpreviously existing software components as a way to reduce development costs, improve productivity, and

provide controlled systems upgrade in the face of rapid technology

evolution

The main goal of COM is promote the interoperability

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Examples:• ArcGIS Desktop (an integrated suite of professional

GIS application) developed by Environmental systems

Research Institute (ESRI), is based on a commonmodular component-based library of shared GISsoftware components called ArcObjects .

• Visual Rule Studio (an object-oriented COM-compliantexpert system development environment for windows)developed by Rule Machines; it installs as an integralpart of MS Visual Basic 6.0.

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Examples:• COM technology in developing a DSS (TRANSPOL) for evaluating

transportation polices and projects. It was developed using four COMcompliant commercially available software packages:2005

Microsoft Visual Basic,

Microsoft Access,

ESRI MapObjects, and Amzi Prolog.

• COM-based expert system to assist the GIS analysts in selectingsuitable map projection for their application in ArcGIS softwarepackage.2006 Visual Rule Studio (an object-oriented used to develop the expert system.

The COM technology was used for integrating the expert system withArcGIS9.0, a COM-complaint GIS software package. Its built in macro language, Visual Basic for Application (VBA), was used to

develop the Map Projection application that implements the expert system usingAutomation Technology.

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If a new metro line between two positions needs to bedesigned in a city, how can this task be done by theaforementioned enhanced GIS? It must be emphasizedthat this kind of task is highly knowledge dependent

such as:

Knowledgethe status oftraffic flow

the city‟slayout,

geological andecologicalsituation,

environment,

populationdistribution,

Others

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Advantages of this system: • Some of functional modules used for problem solving such Knowledge

base and state evaluation function, which is the key to searching in statespace are open and can be updated according to a concrete application.

• The rules can be revised, added and deleted. The rule base can beupdated easily. This keeps the system in accordance with the latestinformation.

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Helps in estimating damage levelFacilitates decision-making and planning

for disaster managers.Allow managers can estimate disaster

area and number of victims to determineand allocate required resources.

Due to the time-sensitive nature ofdisasters, rapid and reliable decision-making has a significant impact on

managing disasters.

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GIS concerned spatial data handling forinput to an expert system.

ES estimates the damage level in the caseof earthquake

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To estimate destruction for an individualbuilding:• Firstly: the earthquake magnitude at the location of

a given building should be calculated.• Secondly: by using some properties such as

structural characteristics, age of buildings, numberof floors, soil type, geological characteristics and

distance of building from the center of theearthquake the expert system and its defined rulesshould be obtained.

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The properties should be classified intoclasses of high and low priority of weightto obtain destruction probability.

This is done through a two step weightingprocess:

1. Each property is externally weighted based on

degree of importance in destruction probability.2. Each property is internally weighted.

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The acceptability of the systemdepends to a great extent on thequality of the user interface.

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It consists of:1. Expert System (ES)

2. Geographical Information System (GIS)

3. Farm database system.

Integration occurs between thosecomponents

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The integration between the ES & GISoccurs through the database.

The database stores all the data about thesoil, water, and climate for each location.

Those data comes from two sources.1. The result of the analytical part of the

geographical information system.

2. The second resource of those data is the user

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There are two types of databases:1. Static farm data

2. Dynamic farm database

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Keep all static farm data as:• Soil data

• Water data

• Climate dataStatic and not changed for short period.Stored according to the location and its

characteristic for soil, water, and climate.Fully integrated with the GIS because it is

follow the spatial data that comes from theGIS.

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Dynamic farm database is to document allevents in the farm during the year.

The farm database will cover:• irrigation

• Fertilization

• plant care

• protect operations• treatment operations

• disorders

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The expert system give strategic advice inadvance either before planting or duringgrowing season.

These sub-systems are• variety selection

• land preparation

• Planting

• irrigation

• fertilization

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This sub-system advises the users about the mostsuitable variety for his plantation based on:1. specific circumstances of the farm2. user requirements

The domain knowledge of this sub-system contains twomodels:1. Suggestion2. Selection

The inference knowledge contains three inference steps:1. Specify2. Select3. count

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The suggestion model contains a relation between theenvironmental conditions and the suitable varieties that isused by 'specify' inference step to suggest the cropvarieties suitable for the surrounding environments.

The selection model contains a relation between userrequirements and the corresponding varieties that isused by 'select' inference step to select, the most

suitable varieties reflecting the user requirements.

The 'count' inference step just counts the specifiedvarieties.

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Design shells for integrated system

Design Quality model

Integrated the ES and the GIS with

another techniques such as Data Mining

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GIS + ES + Data Mining

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There are many capability limitations in both expertsystems and geographic information systemsthese limitations make it hard for each of themalone to handle many of real world. Integration

between both ES and GIS can handle thesecapability limitations.

There are many systems that are developed afterintegrating both GIS and ES. These systems help

in solving complex problems in different fields asagriculture, transportations, risk management…etc.

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