APG: A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

نویسندگان

چکیده

One important step in binary modeling of environmental problems is the generation absence-datasets that are traditionally generated by random sampling and can undermine quality outputs. To solve this problem, study develops Absence Point Generation (APG) toolbox which a Python-based ArcGIS for automated construction geospatial studies. The APG employs frequency ratio analysis four commonly used driving factors such as altitude, slope degree, topographic wetness index, distance from rivers, considers presence locations buffer density layers to define low potential or susceptibility zones where generated. test toolbox, we applied two benchmark algorithms forest (RF) boosted regression trees (BRT) case investigate groundwater using three absence datasets i.e., APG, random, selection samples (SAS) toolbox. BRT-APG RF-APG had area under receiver operating curve (AUC) values 0.947 0.942, while BRT RF weaker performances with SAS Random datasets. This effect resulted AUC improvements 7.2, 9.7% dataset, 6.1, 5.4% respectively. also impacted importance input pattern maps, proves points issues. proposed could be easily other hazards landslides, floods, gully erosion, land subsidence.

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ژورنال

عنوان ژورنال: Geoscience frontiers

سال: 2021

ISSN: ['2588-9192', '1674-9871']

DOI: https://doi.org/10.1016/j.gsf.2021.101232