Better results could be obtained by entering into the model only the variables that the investigator assumes to be the most significant. Since input factors are invariably interrelated, the technique of entering all the available variables can produce even worse outcomes with some variables characterised by meaningless coefficients. In discriminant analysis and logistic regression , high and low values of membership probability indicate hazardous and safe terrain-units , respectively. Values close to 0.5 do not provide any additional information with respect to the input landslide map. Hence, the model could be statistically sound, but of limited application.
Any model is unable to correctly classify all terrain-units .
If this should occur, the model , once again, would not provide more information than the input landslide map. The second case indicates either wrong mapping or a model which lacks the factors that caused a landslide in that specific or unique environmental setting. Ideally, a good model should minimise the latter type of misclassification. Owing to these pitfalls, hazard assessment and mapping are intrinsically uncertain operations which nowadays are taking advantage of the opportunities provided by new technologies, such as GIS, but are still requiring new efforts for improving both data quality and model reliability. Under such circumstances, predictive and explanatory models may be quite successful since they rely on a few good predictors of the phenomenon to be forecasted.