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Knowledge discovery from GIS in 'Natural Resources Targeting'
Data Integration and Analysis
Concentration of As and Sb among others, were found to be strong pathfinders of gold occurrences. The Lineament proximity map created using weights of evidence modeling, (Sahoo et al. 2000) was used with the As and Sb concentration maps to predict the gold occurrences. The concentration maps of As and Sb were continuous functions, whereas, the lineament proximity was a boolean map. Pixels lying within this buffer-zone of lineaments were assigned a value of 1, and those outside the buffer-zone were assigned a value of 0. The response map, proximity to known gold occurrences was a boolean map, pixels lying within the 0.5 Km buffer-zone of the deposit occurrences were assigned with value 1 and others were assigned 0.
Modelling with logistic regression is a step-wise procedure of fitting the model. Following steps were carried out during model fitting (Sahoo and Pandalai, 1999).
Step 1
In this step, univariate statistics were computed using each independent explanatory map to establish its statistical significance. Wald's statistic was used to remove the insignificant variables. All the three factors: concentration of As, Sb and lineament proximity were found significant to be retained in the fitted model, at a = 0.05 level.
Step 2
In this step multivariate analysis was carried out with all the main effects, found significant at a = 0.05 level. Using AIC, SC and likelihood ratio test statistic, it was observed that Lineament proximity was not a significant factor in targeting gold occurrences.
Step 3
In this step, Multivariate analysis was carried out using significant main effects and all interaction terms. G statistic was used to evaluate the significance of the main effects and their interactions.
The final model for predicted probability for gold occurrences using lineament proximity map, As and Sb concentration maps, is computed as
Y = 1/(1+ exp( - ( - 6.29 + 3.49 * As + 4.28 * Sb + 3.1 * As * LIN -2.62 * As * Sb * LIN)))
As this method does not involve subjective weighting for each explanatory variables, the error propagation in each step of computation is less. However, the different statistics used in assessment of the model-fit can be manipulated while selecting a model, as per the user's expert-knowledge. The interaction terms are also studied in this model.
Conclusion
GIS-based multi-criteria, multi-objective evaluation using Fuzzy theory, Bayes theory and Belief function is a powerful tool in targeting potential areas for exploration. A major contribution is provided by the interaction of user and expert-knowledge in discovering knowledge about the data. However the procedure of scaling varied data-layers and subjective factor-weighting and rank-weighting leads to propagation of error in an inference net. The unique capability of logistic regression in handling varied data-types, data-driven factor-weighting and modeling factor interactions provides a less erroneous model.
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