SLEMSA 
~ VARYING K 
REVISED SLOPE 
7. E.CANADA 
USLE MODEIL_MUSLE 
/ 
\ 
  
TASK 
  
  
  
   
  
  
  
NOCN 
Q(S’,P 
S',P',CN y 
USDA AMC-II CN METHOD 
', A(Q,S,L,K,C,P) 
Q(CN,S"),A(Q,S,L,K,C,P) 
USGS LUDA CN METHOD 
LANDSAT CN METHOD 
USDA AMC-II CN __Q=(Q(CN,S");A(Q,...) 
USGS LUDA CN .... 
Q=(Q(CN,S");AQ,...) 
  
NO LANDSAT CN Q=(Q(CN,S);A(Q,...) 
USLE PRMS ALL PARAMETERS KNOWN A(R,S,K,L,C,P) 
NOR SLOPEUNKNOWN ___ NO SOLUTION 
—NOK 
NO 
NOC,P 
m 
S 
NOL 
LENGTH «-4m, S = S(SLOPE) 
1.1%<SLOPE<9%,S=S'(SLOPE) 
9.1%<SLOPE<30%,S=S"(SLOPE) 
Figure 4: Knowledge About Soil Loss Estimation and Modelling Processes(Source, 
  
  
  
  
  
  
  
  
   
   
   
E.G. Mtalo, 1990, page 168). 
WISCHMEIER ____ 
HOLY PERIOD C'=15 
fo USDA: PERIOD 2 C" 2 15 
METHO PERIOD 3 C'=15 
PERIOD 4 C'=15 
C-FACTOR C PERIOD 5 C" 225 
LOCATION....F REDERICTON 
GRANDFALLS POTATO,GRAIN & HAY (P/P/P/G/H) __C=0.23 
POTATO(PPPP) son = 0.53 
GRANGGGS) D m OLD 
POTATO&BROCCOLI(P/B) C = 0.56 
POTATQ&GRAIN 
(P/P/P/P/G) ___ C=048 
(P/P/P/G) ____ C=0.40 
(P/P/G)__. C=0.38 
(PO) C=032 
  
(P/G/...): Crop Rotation; e.g. Grain After Potato... 
HOLY: First year corn after meadow, residue left and incorporated by ploughing. 
PERIOD n: Crop stage n. 
Figure 5: A Segment of Knowledge On Selection of USLE C FACTORS 
systems technology. Their response was generally favorable 
and they pointed out the need to carry out a more exhaustive - 
investigation for the purposes of refining the prototype. 
3.2 A Structured Approach to the 
Data and Facts. 
Compilation of 
The LEARN sub-system is an expert shell which captures and 
organizes knowledge in the form of a semantic network of 
English-like subject-verb-object triplets. Knowledge 
representation using the LEARN sub-system involves the 
analysis and re-structuring of domain knowledge into Subject- 
Verb-Object (SVO) triplets where, "Subject", represents a 
domain concept, "Verb", represents a semantic relationship and, 
"Object", represents an attribute or property of the "Subject". If 
202 
the "Object" part is a phrase assigning value to an attribute of the 
object then the SVO triplet is equivalent to an "Object-Attribute- 
Value" (OAV) triplet. 
The query processor of the LEARN sub-system consists of 
several specialized inference operators, collectively, referred to 
as the inference layer (Figure 3). These operators exploit 
semantic links among the SVO object triplets to determine class 
relationships and extract the attribute values of the stored 
triplets. A section of the LEARN sub-system operators and their 
functional characteristics are shown in Figure 7 where S, V, O, 
SV, SO, VO and SVO are basic search operators, (S,v,0) 
represents the search space (database) and (S), (V), (O) and 
(True, False) represent solution spaces.