Artificial intelligence approach to paleogeography and evolutionary trend analysis of Laurentian brachiopod fauna in the Rhynchotrema-Hiscobeccus lineage.

چکیده مقاله

The current study proposes the first report of employing an artificial intelligence model based on neural networks to map a set of quantitative morphological measurements of the Rhynchotrema-Hiscobeccus lineage of North America to their locality and evolutionary trend. For this purpose, a total of 171 morphometric measurements of the Late Ordovician brachiopods from 11 localities of North America were divided into 114 training and validation samples and 57 testing sets. The input morphometric parameters of the studied brachiopods include shell length (L), shell width (W), shell thickness (T), sulcus depth (T1), sulcus maximum width (W1), sulcus floor width (W2), apical angle (AA), lamella-covered shell length (L1), lamella number (Ln). Artificial neural network tries to find a mathematical formulation between the mentioned morphometric parameters and their corresponding localities from North America. The results showed that the accuracy of the neural network approach in estimating the locality of the testing brachiopod samples is 81%. In the light of satisfactory results of neural networks, having a set of the morphometric data from the unseen Late Ordovician brachiopods, their localities can be estimated.