Graduate School of Humanities

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GEO500B5（地理学 / Geography 500）
Lecture:Physical GeographyⅠ

中山　大地

Class code etc
Faculty/Graduate school Graduate School of Humanities
Attached documents
Year 2022
Class code X0451
Previous Class code
Previous Class title
Term 春学期授業/Spring
Day/Period 金3/Fri.3
Class Type
Campus 市ヶ谷
Classroom name 各学部・研究科等の時間割等で確認
Grade
Credit(s) 2
Notes
Class taught by instructors with practical experience
Category 地理学専攻(博士後期課程)

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Outline (in English)

Course outline;
We will read the textbook in English that describes how to process digital elevation models. Programming in Python will be conducted based on the content of the textbook. Then, we apply decision trees, a type of machine learning, to the results to create a landslide discrimination model and a landslide prediction model, and produce a hazard map.

Learning objectives;
(1) Understand the principles of processing digital elevation models, which are representative of raster-type data, and be able to perform numerical calculations based on the principles.
(2) To be able to quantitatively evaluate the performance of the discrimination model using various indices obtained from the confusion matrix.
(3) To be able to create a hazard map based on a landslide discrimination model.

Learning activities outside of classroom;
Approximately 2 hours of prior study per week is required, especially for literature reading and programming.
Both the first and second semesters include practical training using GIS. Reviewing outside the class (at least 2 hours per week) is necessary because the practical training is not completed in the class.

Grading criteria/policy
(1) Grading method
Ordinary points and presentations
(2) Grading criteria
The degree of application of the course content to your own research project (40%) and the degree of understanding of the lecture content (40%) will be considered. In addition to this, the presentation (20%) will be evaluated comprehensively.

Default language used in class

日本語 / Japanese