1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际课需要或在课程检讨之后产生变动。如对课程有任何疑问,
联系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
灾害风险分析导论 Introduction to Catastrophe Risk Modelling
2.
授课院系
Originating Department
地球与空间科学系 Department of Earth and Space Sciences
3.
课程编号
Course Code
ESS322
4.
课程学分 Credit Value
2
5.
课程类别
Course Type
专业选修课 Major Elective Courses
6.
授课学期
Semester
春季 Spring
7.
授课语言
Teaching Language
英文 English
8.
他授课教师)
Instructor(s), Affiliation&
Contact
For team teaching, please list
allinstructors
Arnaud MIGNAN,风险研究院&地球与空间科学系
邮箱:mignana@sustech.edu.cn
办公室:创园 6 511-3
Prof. Arnaud MIGNAN, Institute of Risk Analysis, Prediction and Management,
Academy for Advanced interdisciplinary Studies; Department of Earth and Space
Sciences
Email: mignana@sustech.edu.cn
Office: 511-3, 5th floor, building 6, Innovation Park
9.
验员/、所、联
方式
Tutor/TA(s), Contact
待公布 To be announced
10.
选课人数限额(可不)
Maximum Enrolment
Optional
2
11.
授课方式
Delivery Method
讲授
Lectures
实验/
Lab/Practical
其它(具体注明)
OtherPlease specify
总学时
Total
学时数
Credit Hours
26
6
32
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
MA212 概率论与数理统计、CS102B 计算机程序设计基础 B
MA212 Probability and Statistics, CS102B Introduction to Computer Programming B
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
14.
其它要求修读本课程的学系
Cross-listing Dept.
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
本课程将介绍采用模型对巨灾风险进行预测和评估的基本原理和方法。内容涵盖各种类型的灾害,即讨论自然灾害
(例如地震、暴风、传染病),也包括人为灾害(例如工业事故、异常断电),学习如何从定性和定量的角度研究这些灾
害的特征,学习对灾害进行评估的技术。经过本课程的学习,学生将掌握风险建模的理论和实践,并熟悉统计分析和计算
常用的 R 编程语言。
This course will introduce the principles of catastrophe risk modelling. We will explore the rich universe of perils, both
natural (e.g. earthquakes, storms, epidemics) and man-made (e.g. industrial accidents, blackouts), study their
characteristics both qualitatively and quantitatively, and develop techniques for their assessment. We will get familiar
with the theoretical and practical aspects of risk modelling, as well as with R programming for statistical analysis and
computing.
16.
预达学习成果 Learning Outcomes
完成课程后,学生将掌握以下内容:
1.了解灾害的不同类型和规模及其对我们社会的潜在影响;
2.巨灾风险建模的基本知识,包括理论和实践方面;
3. R 统计编程的基础知识(概率分布,参数估计,模拟,绘图);
4.基本物理概念的基本知识(能量,幂律,网络,公用事业等);
5.贝叶斯推理方法的基础知识;
6.实践动手能力:批判性思维、实用主义、以目的为主的原则、第一性原理、团队合作精神。
Upon completing the course, students will master the following items:
1. Understanding of the different types & scales of hazards and their potential impact on our society;
2. Fundamental knowledge of catastrophe risk modelling including both theoretical and practical aspects;
3. Basic knowledge of R statistical programming (probability distributions, parameter estimation, simulations, plotting);
4. Basic knowledge of fundamental physical concepts (energy, power-law, network, utility, etc.);
5. Basic knowledge of Bayesian inference methods for forecasting;
3
6. Hands-on knowledge: Critical thinking, pragmatism, top-down view, first physical principles, teamwork.
17.
课程内容及教学日历 (如授课语言以英文为主,则课程内容介绍可以用英文;如团队教学或模块教学,教学日历须注明
主讲人)
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
Chapter 1: Overview of Catastrophe Risk (2 hours)
Week 1 (lecture 1): Concepts & terminology, such as 'disaster', 'hazard', 'risk', 'probability', 'uncertainty' and 'model';
History of catastrophe risk (myths, infamous disasters, scientific & engineering milestones); Types of perils, defined by
category, such as natural (extra-terrestrial, meteorological, geological, hydrological, biological) or anthropogenic
(accidental, malicious), defined by scale & frequency (low to high impact, common to very rare); Syllabus overview &
what the student is expected to learn throughout the course [assignment #1: Describe 5 perils not discussed in class &
classify them].
Chapter 2: Basics of Catastrophe Modelling (4 hours)
General framework describing hazard assessment (source definition, event frequency and severity) & risk assessment
(exposure, vulnerability, risk metrics); Application to various natural & anthropogenic hazards discussed in Chapter 1,
conceptualised and compared using first physical principles. Fundamental concepts to be addressed include energy
release, power-laws, and probability distributions, illustrated with basic R codes for an initiation to this programming
language.
Week 2 (lecture 2): Hazard part [assignment #2: Download R & run provided hazard code]; Week 3 (lecture 3): Risk part
[assignment #3: run provided full risk code, the template for the main student project].
Chapter 3: Probabilistic Seismic Risk Assessment (4 hours)
Detailed framework based on the concepts learned in Chapter 2 but with application to seismic risk assessment;
Concepts of probability distributions for hazard curve, vulnerability/fragility curves and loss curve definition. Calibration
methods to estimate hazard, vulnerability and risk from historical data; Engineering approach to seismic risk
quantification. A full R program will be studied in detail and proposed as template for the student-group project (transfer
to other risks, of their choice).
Week 4 (lecture 4): Hazard part [assignment #4: Modify input parameter values & compare resulting hazard curves];
Week 5 (lecture 5): Risk part [assignment #5: Compute damage ratio expected for different earthquake magnitudes].
Student project session A: Brainstorming & early-stage programming (2 hours)
Week 6 (interactive session): Interactive session during which the students formally decide which risk they want to
quantify (by groups); Feasibility analysis, data accessibility, transfer of knowledge from chapters 1-3. Start to draft their
model in a report and R code. Any peril may be considered for this project except earthquakes (i.e. provided template).
Chapter 4: Hazard Footprint Modelling for different Classes of Perils (4 hours)
Presentation of different approaches for the modelling of hazard footprints of different classes of perils, moving from the
diffusion processes mentioned in previous chapters to: Brownian motion (e.g. storm tracks), cellular automata to model
interface events (landslides, wildfires, tsunamis) and site-specific conditions (e.g. seismic wave), and networks to model
network-based events (epidemics, lifeline failures, such as blackouts).
Week 7 (lecture 6): Concepts of Brownian motion and cellular automata [assignment #6: Run the provided cellular
automaton code & plot the resulting footprint]; Week 8 (lecture 7): Concepts of network theory [assignment #7: Run the
provided network propagation code & plot the resulting footprint].
Student project session B: Consolidation of program & first results (2 hours)