Classification of Ground-Truth Fire Debris Samples Using Neural Networks

• NCJ Number: 306207
• Author(s): Nicholas Thurn
• Date Published: 2021
• Length: 137 pages
• Grant Number(s): 2017-IJ-CX-0023
• Agencies: NIJ-Sponsored
• Publication Type: Thesis/Dissertation

Annotation

This paper reports on the classification of ground-truth fire debris samples using neural networks and a subset of the ions specified in ASTM E1618-19, which represent many of the compounds present in ignitable liquids, to cluster and classify ground-truth fire debris samples.

Abstract

The first part of this work demonstrates that these ions provide sufficient information to allow for the clustering of the ignitable liquid classes defined in ASTM E1618-19 and substrate pyrolysis extracts using self-organizing maps. Classification using self-organizing maps resulted in a 96% correct classification rate on an independent test set. The latter portion of this work demonstrates the use of the ASTM ions in conjunction with feedforward neural networks to evaluate laboratory prepared ground-truth fire debris samples. An optimal neural network model was selected from a set of candidate models that were trained on in-silico fire debris samples. Receiver operating characteristic curves were used to select an optimal decision threshold for classifying a fire debris sample as positive or negative for ignitable liquid residues using a false positive to false negative cost ratio of 10. The use of this threshold for classification resulted in a somewhat conservative model with a true positive rate of 0.59 and a false positive rate of 0.07 for a set of laboratory-generated ground-truth fire debris samples. (Published abstract provided)