Filling in the gaps in the NDFF

Abstract

Our research is distributed into the following stages:

A. The production of a "ready for analysis" data set with attributes of a location or area listed in each row together with the matching occurrence counts. We assembled data from many sources, choose the appropriate level of aggregation (such as area size), and included fabricated 0 counts.

B. Using machine learning algorithms such as linear regression, support vector regression, random forests, etc., analyze this data set to produce predictive models. Additionally, this section aims to provide light on the characteristics/features that are crucial for predicting the presence/absence of a species.

C. Reviewing the developed models.