R2.1 Machine Learning Approaches

27 Sep 2018
13:15 - 14:30
Lecture Hall 2

R2.1 Machine Learning Approaches

R2.1 _ Understanding species distribution, population dynamics and phenology by machine learning. (Session Chairs: Antonino Staiano, Alberto Lanzoni and Friedrich Recknagel)

Inferential modelling by machine learning techniques allows efficient and user-friendly analysis and synthesis of highly complex ecological data. Methods like random forest, quantile regression forest, Maxent, GARP have been successfully applied for species distribution modelling of extensive spatial data resulting in species response curves that describe a species’ response to a given habitat condition, and illustrate specific habitat requirements for the species. Non-supervised artificial neural networks, regression trees and canonical correspondence analyses allow to ordinate and classify complex ecological data. Applications of support vector machines, supervised artificial neural networks and evolutionary algorithms to large spatial and temporal data allow to predict population dynamics and reveal phenology.

This session welcomes papers on all aspects of inferential modelling of ecological data by means of novel machine learning techniques.

Talk 15-16

Remote Sensing based Estimation of Forest Biophysical Variables using Machine Learning Algorithm
Ritika Srinet, Subrata Nandy, and N. R. Patel

Maxent modelling of spiked pepper (Piper aduncum L.) in Mindanao, Philippines
Rowena Japitana and Damasa Macandog