|
Using ESTA to develop expert system for the Natural Resource Management
3. Knowledge Acquisition:
Knowledge acquisition is one of the most important phases of the expert system development cycle in which domain specific knowledge elicited from human expert or other related sources are transferred to the knowledge engineer. The acquisition of knowledge for the proposed system has been carried out by making consultation with human experts and standard literature referenced above. The key features of each land use and land cover category were discussed in details. Efforts have been made to collect more and more heuristic knowledge about their identification. Details of it can be found in [20]. 4. Knowledge Representation Logic has been one of the oldest tool for diagnostic problem solving and logic based different diagnostic approaches like abductive diagnosis, deductive diagnosis, consistency based diagnosis etc have been developed [21]. ESTA also supports rule based knowledge representation in the logical paradigm. From acquired knowledge identification rules for different land use and land cover categories were developed. For example following rules have been included in the knowledge base to identify Rule 1.If there occur white patches along the river Then identify sand. Rule 2.If there is bright blue or dark blue long non-linear long lines (thick/thin) Then identify them as river. Obviously, when system will trigger rule1 first it will look for the ident ificat ion of the river. Thus while triggering rule 1 system first fires rule2. There are two major knowledge representations in ESTA namely Section and Parameters. Section is top level of knowledge representation and contains the logical rules that directs ESTA how to solve problem, actions to perform such as giving advice, going to other section, calling to routines etc. The first section is always named as start section. Parameters are used as variable and it decides the flow of control among the sections. ESTA accepts four types of parameters namely Boolean or logical, Text, Number and Category parameters. These parameters serve different purposes. The Boolean parameter is used when the answer to asked question is either Yes, No, or Unknown. Text parameters are used for text object. Number parameter is used to represent numerical values. Category parameters are used when variable takes more than one values. The value for any of the parameter is calculated from end-user’s response to a question, through other result of application of rules. Any parameter consists of declaration of field, type field and number of optional field such as explanation field, rules field, picture field, question field etc.In the screen short of Fig.4 all such fields a Boolean parameter agri_q2 (name is arbitrary) of agricultural use section (a class of land use and land cover) is Shown. Each land cover and land use category has been represented in the separate section. The screen shot from the implemented system are shown the Fig.5 and Fig.6.Fig.5 shows interface of the system for the user which also shows details of  Fig.4 Screen shot of a parameter agri_q2 each option in the figure window. Fig.6 represents internal representation of the rules in a section that transfer controls in accordance with the user’s response. Each section contains classification rules placed in first come first serve order. The feature corresponding to FCC interpretation keys are placed in these backward chained rules including parameters. Each parameter contains explanation for questions, if needed rules to decide value of parameters, question statements and picture related to the said  Fig.5 Screen shot showing different land used and land cover categories question. These all are shown in the screen shot of Fig.6.Design of the system follows top-down design approach.The developed knowledge base is stored in the compiled mode for faster consultation. Consultation and Result: The system has been used to interpret IRS-1B based FCC image,as shown in Fig.7, of the Lalganj area of the district of Vaishali in Bihar. The classified land use and land covers are shown in the Fig.8. The result shows consistency of the knowledge base and suitability of diagnostic reasoning in representing visual photo interpretation skill of FCC images. Since the system is developed in symbolic techniques so the rules used by the system can be seen and line of reasoning can be understood. Such system is especially useful for newcomers in the area of visual image interpretation and remote sensing.  Fig.6 Screen shot showing a section References:
 Fig 7 FCC image taken by IRS-1B and IRS-1D  Fig.8 Identified Land use & Land cover categories  Fig.3: Hypothetico-Deductive diagnosis framework
Page 3 of 3
| Previous | |