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N. Jafari:
"An Empirical Approach to Risk Modeling in Brownfield Regeneration";
Supervisor: W.S.A. Schwaiger; Inst.f.Managementwissenschaften, 2017.

Over the last decade, regeneration of derelict and underused sites with
varying degrees of contamination (also known as Brownfield sites) has
gained popularity as a sustainable land use strategy. However, redevelopment
of contaminated fields is a complex and multidimensional problem
that entails many risks and uncertainties. The objective of this thesis
is to construct, calibrate and validate a risk assessment model that can
assist investors and decision-makers in evaluating and classifying brown-
field sites to two categories : suitable for redevelopment / not suitable for
redevelopment. The three-step model building process is adopted from
the methodology of credit risk modeling used in banks and credit rating
agencies. The proposed models utilize two machine learning algorithms,
namely Classifcation And Regression Trees (CART), and Random Forest
algorithms. The first part of the thesis provides a point of reference in
brownfield regeneration risk modeling and describes the current research
gaps in this field. The following chapter describes the credit risk model
building methodology. Finally, Chapter 4 describes the implementation of
risk model building methodology in the field of brownfield risk modeling
using programming language R. Appendix A includes the commented Rcode
for interested readers and can serve as a guideline in implementing
the Classification And Regression Tree, and Random Forest algorithms in
various fields of study.