Role of Expert System in Natural Resources Management
3. Remote Sensing and knowledgebase:
In the recent years the technique of remote sensing has shown its superiority in data
collection for natural resources management. It has been recognized that the value of data
which is collected by known conventional means is considerably enhanced by the use of
remote sensing and air-photo interpretation techniques which in turn calls for data of
ground truths. The technique of remote sensing has been applied in almost every aspect
such as atmosphere, geosphere ,biosphere, hydrosphere, and cryosphere together with
environmental application and data collection systems etc. of natural resources
management. Remotely sensed data/images are used to obtain necessary information on
land under various crops, crop rotation and agricultural practices adopted, soil types,
problems of land degradation, availability of water bodies (both surface and ground
water) etc., which are very useful for agricultural development. The remotely sensed
data/images can be taken even of inaccessible land and identification of unused land,
waste land, degraded land etc. can be done by applying suitable technology and
agricultural practices. The repetition coverage of space remote sensing is useful in
detecting changes/degradation, unwanted happening, and correct measures can be taken
in advance.
Remote sensing data/images have been used in water resource
management in citing various recharge structures through the preparation of thematic
maps on land use/land cover, geomorphology, surface water bodies etc. and their
combined analysis. Based on the land cover, slope, soil etc. it is possible to priorities
areas in watersheds where there is need for immediate a forestation or other treatment to
conserve soil. Satellite remote sensing data are useful in carrying out integrated
sustainable development planning [12] at manageable units. The remote sensing data can
be used for the preparation of a set of resource maps such as surface water bodies, ground
water potential zones, ground water recharge site, type of soil, existing land use patterns
etc. and the combination of these data with other information like meteorological data,
socio-economic factors etc. can be used to identify the priority areas for various land use
to meet the needs of the people without disturbing the ecology.
The application of remote sensing in agriculture has also produced
praiseworthy result. Now remote sensing technology is capable of providing information
about various agricultural resources which influences agricultural production directly viz.
land, water and weather and also the related one such as forests and access to other
agricultural information.
4. Expert System and Natural Resources management:
The knowledge related to natural resources may be structured or unstructured and
can be organized in highly structured form to meet the requirements for making
utilization of information received form the various sources. This calls for the utilization
of Artificial Intelligence and Knowledge Engineering methods to represent and infer with
such knowledge; software engineering techniques to manage system developments,
information and control flows of models and data; intelligent system technology to
process and display data. The domain specific knowledge used in decision-making can be
represented in symbolic or asymbolic formalism in the most explicit and formal manner.
The knowledge representation scheme of natural languages is the most sophisticated and
this very act of knowledge representation have been captured by logic, and different
formal methods such as propositional logic, predicate logic, fuzzy logic, semantic
networks, frames etc. and related techniques have been developed to represent various
types of knowledge that can be used by expert systems in decision making and reasoning.
In the recent years the asymbolic approach [6] for intelligence modeling has evolved in
which neural networks, modelled after human brain, are connected to represent
knowledge and to make inferences. In such systems the knowledge is encoded by
connection strength and acquired through learning process [7]. In geographical analysis,
decision-making system like intelligent spatial decision support systems [5] have been
developed to reason with structured or loosely structured knowledge. Here, again the
expert system shell play core role in directing control flows and information flows. It
provides facilities to represent and store domain specific knowledge acquired form
experts or learning examples. It can also contain meta-knowledge for inference control
and possess capacity of reasoning and inference of decision support system. It is the brain
of decision support system. In another attempt by Leung [8], SDSS shell has been
developed using fuzzy logic based expert system shell for the purpose of building SDSS
to solve specific spatial problems in effective and efficient manner.
The object oriented programming approach has been found to be very
effective approach in developing systems for the natural resources management. The
object-oriented approach provides the way for user to perceive reality. It provides an
effective user interface, enhances data reusability, maintainability, and extensibility
through data encapsulation and inheritance. The object oriented database system
overcomes many limitations such as limited query processing, lack of semantics, lack of
extension mechanism, lack of handling recursions and data version etc. of relational
database and thus provides somewhat better option for application in expert system.
Based on this approach Gahanna [9], Worbys [10] have developed system for
geographical information manipulation and decision-making. Most of the current object
oriented G I S designs emphasise processing of geometric data models and data
structures. However, the concept based object oriented GIS by Leung [11] provides a
spatial conceptual model which comprises spatial semantics, fundamental to spatial
analysis, and an object oriented data model which provides an appropriate and effective
representation of the spatial conceptual model for better database management.
The remotely sensed data require their analysis, interpretation and preparation
of databases at various levels of use and application in different decision-making
systems. The need of interaction with these databases in the monitoring and identification
of earth resources creates the opportunity for application of Artificial Intelligence in
general and that of Expert Systems in particular. Different expert systems have been
developed and are being developed in different institutions for every stage of application
of remote sensing techniques in the natural resources management. National Remote
Sensing Agency (NARSA), Bangalore has developed an expert system [13] in PROLOG,
to access the databases of acquired/processed remote sensing data. This expert system
provides access to databases by making queries relevant to type of data needed for a
particular application. NARSA has also developed expert systems for interpretation of
remote sensing data / images emulating the experiences and logical reasoning process
used by human experts to derive information from remote sensing data/ images. Such
expert systems [14] have been developed for interpretation in the area of soil studies, land
use, land capability, geology etc. GIS use remotely sensed data of natural resources along
with various ancillary information such as map data, statistical data, meteorological data
etc. In such applications AI is again needed to interpret ancillary data for GIS and to
provide natural language interface to GIS. Very few such successful systems have been
developed. A rule based expert system to identify land use/ land cover using satellite data
has been developed by K. Ramani and N. C. Guatam [15] . The other expert system [16]
for image interpretation taking into account the elements of image interpretation for
identification of land use / land cover classification, has been developed at APOLLO
work station. This expert system has been developed in PROLOG. An expert system for
soil classification was developed by Rao, et al [17] in M-PROLOG that can make
identification and classification of soil up to great group levels. Several other expert
systems have been developed and used successfully in various areas such as land form
study, ground water, minimal exploration etc. For the urban planning [18] and urban
scene analysis, different systems have been described by different scientists using
different AI techniques. VISIONS system by Hanson and Riseman [19] , SPAM system
by Meken [20] , are some examples of expert systems used in urban scene analysis.
Junsen [21] has described a prolog-based approach for urban analysis. This incorporates modules for roads, rivers, settlements, texture analysis etc. It uses both the qualitative and
quantitative knowledge about urban structures. Nagao, Matsuya, and Mori [22] have
developed system for aerial urban scene analysis using black board approach. In this
system there have been designed two object detection sub-systems that handle different
subgroups of objects like roads, house etc. These sub-systems are picture driven and
model driven. Picture driven system searches the existence of local areas by combining
the characteristics regions while the model driven system picks up the candidate region
by using the spectral and spatial relationship with already recognized objects.