 This paper presents a comprehensive data-driven modeling experiment, evaluating multiple techniques including neural networks, genetic programming, evolutionary polynomial regression, support vector machines, M5 model trees, and k-nearest neighbors. It also proposes five datasets from Canada and Europe for use in the modeling experiment and discusses the results and analysis of the experiment. This article was authored by A. L. Shorbighi, G. Corzo, S. Srinivasulu, and others.