Extreme Learning Machines for spatial environmental data

• We propose a methodology for spatial prediction using Extreme Learning Machine (ELM).
• We examine the results through residuals analysis for real and simulated data.
• We highlight the abilities of ELM to characterize feature relatedness.

The use of machine learning algorithms has increased in a wide variety of domains (from finance to biocomputing and astronomy), and nowadays has a significant impact on the geoscience community. In most real cases geoscience data modelling problems are multivariate, high dimensional, variable at several spatial scales, and are generated by non-linear processes. For such complex data, the spatial prediction of continuous (or categorical) variables is a challenging task. The aim of this paper is to investigate the potential of the recently developed Extreme Learning Machine (ELM) for environmental data analysis, modelling and spatial prediction purposes. An important contribution of this study deals with an application of a generic self-consistent methodology for environmental data driven modelling based on Extreme Learning Machine. Both real and simulated data are used to demonstrate applicability of ELM at different stages of the study to understand and justify the results.