Development of a Risk Assessment Mathematical Model to Evaluate Invasion Risk of Invasive Alien Species Using Interval Multivariate Linear Regression

Evaluation of risk of Invasive Alien Species (IAS) with uncertain and imprcise data is a challenging task. In the present work, mathematical model for risk assessment is developed by using interval multiple linear regression analysis in which mimic unceratin and imprecise data. Here both dependent and independent variables are interval-valued.

12 invasive attributes selected as model parameters. Proposed a new method find the solution of design matrix using interval least square method. Here obtained a dataset of 28 invasive plant species which contains single-valued observations of 12 parameters and invasion risk scores which are obtained from National Risk Assessment. Using the dataset formed four interval input datasets. New method is proposed to find the estimates for interval regression coefficient using interval least suqare method. The interval regression coefficents are estimated using four different interval input data set. The quality of the approximated model is evaluted by average accuracy ratio and the models are validated using well known six invasive and four non invasive species.

The approximated model gives average accuracy ratio of 0.730852 along with data set 3 which is the highest among all data sets. Validation results show that the expected risk score of each plant species from National Risk Assessment is within the approximated risk interval.

Comparing the quality and the validation results, it is found that the approximated model along with data set 3 gives better predictions of risks of invasive alien species if its invasion is dominated by biological traits.