Data Capture 
_ Info Requirements 
  
  
  
  
  
  
  
Data Processing 
Information Extraction 
  
  
  
Y 
  
  
  
  
  
  
  
  
  
   
  
Knowledge Base 
[rman } 
Y 
S 
oil Properties 
Ground Cover 
Hydrologic info 
Meteorologic info 
Topography 
  
  
Erosion State 
    
  
  
    
Human Expert 
  
  
  
  
Rules | 
| Expert System | 
  
  
  
  
  
  
Field & Laboratory EEE 
GIS & DBMS ) 
Computer 
IAS & Topo Maps al 
qe Parameters 
Conservation Practice 
Soil Loss Estimates 
ifm 
Crop Estimates Facts 
(ER J reet | 
Knowledge Utilization ul — — —] 
Knowledge Extraction 
  
  
  
  
  
Figure 2: A Conceptual Model of the Soil Loss Estimation and Modelling Process(After 
E.G. Mtalo, 1990, pp.23). 
3. SYSTEM DESIGN AND COMPONENTS OF SLEMS. 
SLEMS is an experimental knowledge based system prototype 
designed and developed for application in soil loss estimation 
and modelling as part of an Msc. thesis research at the UNB 
CanLab-INSPIRE in 1990 (Mtalo, 1990). At the current level of 
development the SLEMS can only manipulate attributive data 
and information. SLEMS consists of the following sub- 
systems(Figure 3): 
- The EXPERT, 
The LEARN, 
The FUZZ, 
The Data Base Management System. 
The first three sub-systems perform knowledge based 
functions. The EXPERT and the LEARN sub-systems are 
based on algorithms and source code published by Schildt 
(1987), modified and augmented for SLEMS implementation 
(Mtalo, 1990). The FUZZ sub-system was developed to 
process fuzzy knowledge. The Data Base Management System 
based on the CDATA (Stevens, 1987) performs conventional 
database management functions for the system. The inference 
layer consists of operators which exploit the semantic links 
between knowledge base objects to facilitate extraction of facts 
not directly stored in the knowledge base. The low level 
operators are basic search operators. 
All the system modules were written in the C programming 
language and implemented on a SUN 4 Work Station. Since 
then a demonstration version of the SLEMS EXPERT was 
implemented for DOS based PC. Currently a new DOS version 
based on object oriented programming is being developed using 
BORLAND C++. In the new version the CDATA interface will 
be replaced by a new one based on the Paradox Engine. As 
mentioned in the introduction the primary objective is to produce 
a system which is affordable by developing countries. 
200 
3.1 The Rule Based EXPERT 
The EXPERT is a rule based expert shell whose main function 
is to capture procedural and classification knowledge in the form 
of English-like "IF ... THEN ..." rules. Given certain facts the 
EXPERT uses a backward chaining search strategy to locate a 
rule satisfied by the given facts. If no rule is directly satisfied by 
the given facts the inference mechanism tries to find if there is a 
rule whose conclusion would provide the additional facts 
needed to satisfy another rule. Newly deduced facts are added 
into the knowledge base and used in subsequent inference. 
At a preliminary stage the knowledge required to solve specific 
tasks must first be analyzed and organised into a decision tree 
where branches indicate alternate solution paths or related 
components of the knowledge. There are three objectives to the 
preliminary analysis. The first and most important objective is to 
enable the domain knowledge to be broken down into smaller 
manageable chunks without losing the semantic relationships 
inherent within the body of knowledge. Secondly the resulting 
structure(Figures 4) facilitates easy search of the resulting 
knowledge base. Thirdly the organization of the domain 
knowledge into a semantic network structure enables the direct 
translation of the structure into "IF... THEN..." rules and 
facilitates checks against errors in the generation of rules. 
The structure of the knowledge required to calculate soil loss by 
the USLE model is shown in Figure 4. Based on this the first 
stage in the estimation of soil loss involves the selection of a 
specific model. Using the decision tree model USLE models 
may be classified by geographic area (e.g. Eastern Canada), by 
inventor (e.g. Wischmeier or Holy) or by method, such as, 
MUSLE (modified USLE), SLEMSA (modified soil estimation 
model for Africa), Revised Slope Factor, or Varying K (varying 
soil erodibility factor) as shown in Figure 4. 
If, for example, the modified USLE (MUSLE) is selected then