Presentation at Accuracy 2012
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1) The document discusses how greater geologic complexity leads to greater uncertainty in predictive models. It analyzes data from areas with small and large geologic complexity in Brazil. 2) Models had near perfect accuracy in areas with small complexity but lower accuracy, around 60%, in areas with large complexity due to greater uncertainty from the distribution of faults, lithological boundaries, and lack of geologic knowledge. 3) The authors conclude that improving predictive models requires improving the geologic information available to reduce uncertainties, especially for environmental covariates used in the models.
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Presentation at Accuracy 2012
- 1. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, UFRRJ Ricardo Simão Diniz Dalmolin, UFSM Pablo Miguel, UFSM Florianópolis, SC, Jul 11th 2012
- 2. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Tectonic setting Deformation GEOLOGIC COMPLEXITY = Distribution of faults and lithological Sedimentation boundaries as a patterns function of scale 79 Igneous events Au Hodkiewicz (2003); Ford and Blenkinsop (2008); Ford and McCuaig (2010)
- 3. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Tectonic setting Deformation GEOLOGIC COMPLEXITY = Uncertainty = Sedimentation Lack of knowledge patterns ? Igneous events Hodkiewicz (2003); Ford and Blenkinsop (2008); Ford and McCuaig (2010)
- 4. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel 147 – 478 m
- 5. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Small complexity (volcanic rocks) Large complexity (volcanic and sedimentary) Small complexity (sedimentary rocks)
- 6. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel - 339 sampling points (0 – 20 cm) - soil and land use survey (purposive sampling) - particle-size distribution - transformed to additive log-ratios - terrain attributes - 10 m DEM from topographic maps - multiple linear regression models - stepwise + repeated 10-fold cross-validation - R (packages stats, base and caret) - GRASS, QuantumGIS and SAGA GIS
- 7. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Table 1. Confusion matrix for the whole study area Reference Prediction User accuracy Sedimentary Volcanic Sedimentary 132 16 0.8000 Volcanic 33 158 0.9080 Mapper accuracy 0.8919 0.8272 Accuracy 0.8555 (95% CI: 0.8134 – 0.8911) NIR 0.5133 (P-Value [Acc > NIR]: < 2e-16) Kappa 0.7099 (Mcnemar's Test P-Value: 0.02227)
- 8. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Table 2. Confusion matrix for the small complexity area Reference Prediction User accuracy Sedimentary Volcanic Sedimentary 95 0 1.0000 Volcanic 0 109 1.0000 Mapper accuracy 1.0000 1.0000 Accuracy 1.0000 (95% CI: 0.9821 – 1.0000) NIR 0.5343 (P-Value [Acc > NIR]: < 2.2e-16) Kappa 1.0000 (Mcnemar's Test P-Value: NA)
- 9. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Table 3. Confusion matrix for the large complexity area Reference Prediction User accuracy Sedimentary Volcanic Sedimentary 37 16 0.5286 Volcanic 33 49 0.7538 Mapper accuracy 0.6981 0.5976 Accuracy 0.6370 (95% CI: 0.5499 – 0.7180) NIR 0.5185 (P-Value [Acc > NIR]: 0.003605) Kappa 0.2798 (Mcnemar's Test P-Value: 0.022271)
- 10. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Table 4. Mean ± standard error of absolute prediction residuals Geologic Clay (%) Silt (%) Sand (%) complexity Small 5.20 ± 0.31 6.79 ± 0.37 7.60 ± 0.44 Large 5.19 ± 0.35 12.55 ± 0.67 15.80 ± 0.91 Kappa = 1.0000 60% of the variance Kappa = 0.2798 ln(clay/sand) = – 1.0027474 + 0.0081838 ELEV – 0.0413414 SLOPE – 0.9702653 WET – 0.0107230 CONV ln(silt/sand) = – 0.9136230 + 0.0090393 ELEV – 0.0278960 SLOPE – 0.0151896 CONV – 0.9682048 WET
- 11. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Small complexity Large complexity Small complexity
- 12. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Geology - Maciel Filho (1990) - Sub-horizontal (5º) - Uncertainty? Elevation - Brazilian Army (1970s) - Aerial photography - Uncertainty?
- 13. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Conclusions ● Yes... we can build predictive models using terrain attributes (and other environmental co-variates) ● However... large geologic complexity = large uncertainty = large prediction errors ● Therefore... improving predictive models depends on improving geologic information available
- 14. Larger geologic complexity implies larger uncertainty Alessandro Samuel-Rosa, Ricardo Simão Diniz Dalmolin & Pablo Miguel Future work ● What is the “required” level of accuracy of the prediction models? ● Do the users of soil information understand “uncertainty”? Traditional soil maps “seem” to be very accurate! ● Give an especial attention to the uncertainty of the environmental co-variates (know where we are going to “fail”)
- 15. Larger geologic complexity implies larger uncertainty alessandrosamuel@yahoo.com.br dalmolinrsd@gmail.com tchemiguel@yahoo.com.br Florianópolis, SC, Jul 11th 2012