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A knowledge-based approach to predicting salinty in The South- West of Western Australia

1P.A. Caccetta, 2H.T. Kiiveri, 2F.H. Evans and 3R. Ferdowsian
1School of Computing, Curtin University of Technology
2CSIRO Division of Mathematics and Statistics
3Department of Agriculture Western Australia

Abstract
This paper describes the construction of a knowledge-based system and its application to predicting salinity in the Kent River catchment located in the south-west of Western Australia. The system incorporates remotely sensed data, data derived from digital elevation models, maps produced by experts and data combination rules based upon expert knowledge and expert-derived training data. Bayesian networks are used as the modelling environment, allowing reasoning with uncertainty in the process of combining data. Firstly, data were obtained for three time periods spaced roughly a decade apart. A knowledge-based system was then conceived and applied to the data, with data from one decade used to predict land condition in the following decade. The resulting prediction maps were then compared with independent validation data, giving good results. The maps were used to provide estimates of the historical spread of salinity in the catchment and its likely extent in the future.

1 Introduction
In much of the Western Australian agricultural region, the clearing of land has lead to ..rising saline groundwater, resulting in the loss of previously productive land to salinity. Based on farmer surveys conducted in 1979 and 1989, the Australian Bureau of Statistics reported that 443000ha {2.8%) of previously arable land was lost to salinity at a rate of about 18000ha each year .

Aerial photo interpretation has been used to assess the extent of salinity in some areas [4], but this is time consuming and expensive. More recent efforts [6] [7] [8] ( have used Landsat TM data to estimate the extent of salinity. Typically, statistical I approaches such as maximum likelihood classification are used to classify the data into different landcover classes, which are associated with the land being severely affected, slightly affected and not affected.

The above approaches provide a means of monitoring the extent of salinity but provide no insights into the likely effects that remedial actions such as tree planting will have on the problem, or given the current land status, what areas are at risk of salinisation in the longer term. The latter is imperative for the formulation of remedial measures.

To explore ways of achieving these aims, we developed a knowledge-based system for representing relationships relevent to salinisation which can then be applied to mapping and predicting salinity. The system takes various input maps in a GIS and applies rules, which have varying degrees of confidence, to produce output maps of existing and likely future salinity. The system may also be used in an interactive fashion to answer what-if scenarios.

The system was developed in the Upper Kent Catchment (approximately 2000 sq. km), which lies in a high rainfall (500- 750mm) area of the agriculture region, approx- imately 350km south east of Perth.

The methodology of our approach was

  1. consult with experts to gain knowledge of the problem
  2. based on the expert knowledge, obtain data (where possible) that is relevant, possibly with further processing, to appling the knowledge over the catchment ‘
  3. based on expert knowledge and available data sets, construct the knowledge-based system
  4. use the resulting model to interpret the data, ie produce salinity risk maps more the region
  5. validate the results by comparing the system output to independent validation data, and if acceptable, form a catchment salinity summary

These points are discussed in this paper .

ACRS 1995

2 Knowledge Elicitation and Relevant Data Sets
A workshop was held to provide a forum to quantify current knowledge on factors relevant to dryland salinisation in the south-west of Western Australia. The workshop ” was attended by 21 people, including experts on salinisation from the Department of Agriculture, the CSIRO and the West Australian Water Authority. The factors identified 4 include time since clearing, depth to ground water, rate of ground water rise, distance to existing salinity, climate, degree of waterlogging, geology, salt storage, landform, position