My PhD research topic is "Developing an integrated flood risk management framework for Vietnam" under the supervision of Dr Von Meding and Dr Kanjanabootra. I have utilised both quantitative and qualitative approaches, but mainly quantitative. In this post I will share some notes on three quantitative methodology approaches applied in my study.
MCDM methods enable us to handle quantitative variables and help decision makers in solving flood management problems such as formulating their values and preferences, quantifying these priorities, and to apply them to decision-making processes. Many MCDM techniques are widely used in the field of flood risk management such as AHP, ANP, CP, ELECTREE, MAUT, PROMETHEE, TOPSIS, VIKOR and SAW. I applied two of these methods in my study; Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) developed by Hwang and Yoon (1981) and Analytic Hierarchy Process (AHP) proposed by Saaty (1988).
The application of AHP is still quite new in the field of disaster management and is gaining more and more attention in the field. It was used to evaluate the criteria and sub-criteria of flood risk components based on decision makers’ judgement in my study. Thankfully, the author of AHP, Professor Thomas L. Saaty, has provided a free software, Super Decisions (http://www.superdecisions.com/) to solve the complicated algorithm of the method. The result using this software looks like this:
3) Machine learning (statistics) approach
I applied regression models and tree-based methods of supervised ML in my study. The statistical R software is very powerful for ML implementation. The book “An Introduction to Statistical Learning” (available download at http://www-bcf.usc.edu/~gareth/ISL/) provides a simple explanation of many ML techniques. The authors of this book also provide many lectures with slides and videos (https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/) to illustrate all contents in the book, so it is very convenient to start learning ML (suitable for beginners like me). This is one of my applications using tree-based methods:
My PhD journey is going to finish at the end of this year. This is the most beautiful and meaningful journey in my life. When walking on this trip, I found some interesting ‘transportations’, including the three approaches that I shared in this post. I hope that it can prove useful for other researchers.