A KNOWLEDGE BASED APPROACH TO THE MANAGEMENT OF SOIL EROSION INFORMATION IN 
DEVELOPING COUNTRIES. 
E.G. Mtalo and E.Derenyi 
Department of Surveying Engineering 
University of New Brunswick 
Fredericton, N.B. CANADA 
COMMISSION VII 
ABSTRACT 
The study and control of soil erosion and the associated environmental degradation is a difficult problem because it involves a very 
wide body of knowledge. The use of knowledge based expert systems in this domain may improve the current efforts by providing an 
efficient tool for inter-disciplinary technology transfer and application. Domain expert systems may also be used as substitutes for 
human experts to provide guidance to less skilled soil erosion domain personnel. 
This paper describes the prototype knowledge based system Soil Loss Estimation and Modelling System(SLEMS) and shows how 
such a system can be used to solve some soil erosion problems. This system was developed and implemented at UNB on a Sun 
workstation for the management and application of remote sensing and geographic information in soil loss estimation and modelling 
problems. 
Key Words: Knowledge Base, Soil Erosion, Environment, GIS, Remote Sensing, Developing Countries 
1. INTRODUCTION. 
Soil erosion is defined as the process whereby detachment and 
transportation of soil from its natural location takes place usually 
with adverse impact on the environment. Factors and causes of 
soil erosion are characterized as: climatic and hydrologic agents 
such as rain, runoff, antecedent moisture, wind, geographical 
position, and altitude; morphological agents including slope, 
slope length and surface roughness; geological and soil agents 
which include soil type, parent material and soil texture; 
vegetative agents such as plant cover type, density and height; 
technical agents consisting of conservation management, tillage 
systems, farm equipment and construction activities; and social 
economic agents such as population density and distribution, 
and other human activities(Wischmeier and Smith, 1957, 
Goldman et al, 1986). Morgan (1986) considers soil erosion to 
be a multi-faceted problem whose solution involves five issues: 
policy, measurement and inventory of soil erosion extent and 
severity, assessment and evaluation of soil loss conservation 
efforts, modelling and estimation of soil loss and social and 
economic issues. 
The five components of the soil erosion management problem 
are strongly interrelated. The various factors influencing soil 
erosion are also intricately intertwined. Clearly soil erosion is a 
highly complex problem whose solution requires harnessing 
and integrating information and technology from a wide range 
of disciplines. Moreover a considerable amount of the 
information needed to model and estimate soil erosion is fuzzy 
or non-precise. At the same time soil erosion evaluation often 
relies on fuzzy decision. 
Monitoring, management, and control of soil erosion in 
developing countries is a critical issue because the economic 
base of most developing countries is mainly agricultural. The 
dangers posed by increased soil erosion to the economies of 
developing countries are therefore very great. 
In the developed world it has become fashionable to handle 
complex problems which require intensive human expertise by 
knowledge based computer systems. This approach extends the 
human ability to analyze complex systems in order to provide 
accurate and timely answers to difficult problems. The expert 
systems technology releases the human expert from tiresome, 
repetitive procedures so that s/he may devote more time on 
designing efficient problem solving strategies. One area where 
the knowledge based systems approach may be beneficial is in 
the solution of soil erosion problems. However at present there 
have been very few expert systems designed to solve soil 
erosion problems. 
In developing countries, where lack of human expertise in all 
fields is often acute, knowledge based systems in the form of 
expert systems could become an important tool for technology 
198 
transfer and application. Domain expert systems can be used as 
substitutes for human experts in providing guidance to less 
skilled personnel in developing countries. 
Assuming an appropriate framework in which local experts 
work together with foreign experts to develop knowledge based 
soil erosion applications the benefits which may be realised 
include: 
(i) Efficient and sustainable transfer of monitoring and 
control technology. 
(i) On the job instruction on problem solving procedures 
by domain expert systems allowing less skilled 
technicians to perform tasks requiring expert 
knowledge. 
Systematic training of local technicians and skilled 
labour on the use and maintenance imported 
technology. 
Reduced dependence on foreign expatriates and 
boosting up self-reliance. 
(iii) 
(vi) 
This paper discusses an experimental knowledge based system 
(SLEMS), developed as part of an academic research, for 
modeling and estimating soil erosion(Mtalo, 1990). Because of 
the need for affordable technology in developing countries the 
system was developed with a view to making it locally 
implementable. The system is therefore designed to provide a 
knowledge based management of attributive information and 
data required for modelling and estimating soil erosion. 
1.1 Knowledge Representation Issues. 
Knowledge based systems, differ from conventional software 
systems by the manner in which the knowledge required to 
solve specific problems is organised. In Knowledge based 
systems the facts (data) and knowledge needed for problem 
solving are extracted and stored in a knowledge base using 
special knowledge representation structures. The knowledge 
may be stored as a set of if...then... rules, frames and semantic 
networks(Firebaugh, 1988, Barr et al, 1989; Luger and 
Stubblefield, 1989). The process of extracting information from 
the knowledge base is referred to as knowledge inference. By 
using deductive and inductive inference techniques, knowledge 
not directly stored in the knowledge base can be inferred. 
Systems which have knowledge about a specific and well 
defined domain of expertise, are called expert systems. The 
introduction of expert systems transformed artificial intelligence 
from a purely research domain into an application field. MYCIN 
and the INTERNIET were among the earliest practically useful 
rule based cxpert systems(Barr and Feigenbaum, 1981,1982). 
Among the few expert systems designed for solving soil erosion 
problems is PLANTING commissioned by the United States