TS0026 Statistical Methods in Water Resources Engineering

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code TS0026
Name Statistical Methods in Water Resources Engineering
Term 2023-2024 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator


Course Goal / Objective

The primary objectives of this course are: a) to teach the statistical methods commonly used in the analysis of data collected in the field of water resources engineering, b) to apply them to real observation data and, c) to contribute to the development of practical water resources projects by interpreting the results.

Course Content

Summarizing hydrologic and hydrometeorologic univariate data sets, graphical data analysis, describing uncertainty, hypothesis tests, testing differences between two independent groups, multiple comparisons of catchment data, correlation and simple regression analysis for completing missing data, multiple regression for hydrological modelling, trend and stationarity analysis, interpreting efficiency of observation numbers in estimation (power calculations for tests))

Course Precondition

In order to enroll this course, it is sufficient to be a graduate or doctoral student. There are no other prerequisites.

Resources

Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p., https://doi.org/10.3133/tm4a3.

Notes

a) Selected articles from national and international journals. User manuals of softwares such as SPSS and MINITAB. b) https://www.sciencebase.gov/catalog/item/5bf30260e4b045bfcae0c205 c) Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical Methods in Water Resources - Supporting Materials: U.S. Geological Survey data release, https://doi.org/10.5066/P9JWL6XR.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Interprets descriptive statistics of hydrological and hydro-meteorological data for project purposes.
LO02 Identifies possible uncertainties by analyzing hydrological data graphically.
LO03 Determines the difference between two independent groups by performing hypothesis tests.
LO04 Decides the homogeneity of the basins by making multiple comparisons on a catchment basis.
LO05 Gains the ability to make hydrological predictions by using multiple regression models as well as correlation and regression analyzes.
LO06 Determines quantitatively the effects of climate change on the trends of hydrological data and applies them to water resources projects.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Has the ability to develop and deepen the level of expertise degree qualifications based on the knowledge acquired in the field of agriculture and irrigation structures 3
PLO02 Bilgi - Kuramsal, Olgusal Has the ability to understand the interaction between irrigation and agricultural structures and related disciplines
PLO03 Bilgi - Kuramsal, Olgusal Qualified in devising projects in agricultural structures and irrigation systems.
PLO04 Bilgi - Kuramsal, Olgusal Conducts land applications,supervises them and assures of development
PLO05 Bilgi - Kuramsal, Olgusal Has the ability to support his specilist knowledge with qualitative and quantitative data. Can work in different disciplines. 4
PLO06 Bilgi - Kuramsal, Olgusal Solves problems by establishing cause and effect relationship 5
PLO07 Bilgi - Kuramsal, Olgusal Has the ability to apply theoretical and practical knowledge in the field of agricultural structures and irrigation department 3
PLO08 Bilgi - Kuramsal, Olgusal Able to carry out a study independently on a subject.
PLO09 Bilgi - Kuramsal, Olgusal Has the ability to design and apply analytical, modelling and experimental researches, to analyze and interpret complex issues occuring in these processes. 5
PLO10 Beceriler - Bilişsel, Uygulamalı Can access resources on his speciality, makes good use of them and updates his knowledge constantly. 3
PLO11 Yetkinlikler - Öğrenme Yetkinliği Has the ability to use computer software in agricultural structures and irrigation; can use informatics and communications technology at an advanced level. 4


Week Plan

Week Topic Preparation Methods
1 Summarizing Univariate Data Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Tartışma
2 Graphical Data Analysis Textbooks, articles and Internet resources Öğretim Yöntemleri:
Beyin Fırtınası, Tartışma, Alıştırma ve Uygulama
3 Describing Uncertainty Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Tartışma
4 Hypothesis Tests Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Beyin Fırtınası
5 Testing Differences Between Two Independent Groups Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
6 Paired Difference Tests of the Center Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Benzetim
7 Comparing Centers of Several Independent Groups Topics covered in the course, textbooks, articles, internet resources Öğretim Yöntemleri:
Anlatım, Gösterip Yaptırma, Örnek Olay
8 Mid-Term Exam Topics covered in the course, textbooks, articles, internet resources Ölçme Yöntemleri:
Yazılı Sınav
9 Simple Linear Regression Topics covered in the course, textbooks, articles, internet resources Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Problem Çözme
10 Alternative Methods for Regression Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Tartışma, Beyin Fırtınası
11 Multiple Linear Regression Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Tartışma
12 Trend Analysis Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Tartışma, Alıştırma ve Uygulama, Beyin Fırtınası
13 How Many Observations Do I Need? Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Beyin Fırtınası, Örnek Olay
14 Discrete Relations Textbooks, articles and Internet resources Öğretim Yöntemleri:
Anlatım, Beyin Fırtınası, Örnek Olay
15 Regression for Discrete Responses and Presentation Graphics Textbooks, articles and Internet resources, supplemental resources Öğretim Yöntemleri:
Anlatım, Tartışma, Beyin Fırtınası
16 Term Exams Topics covered in the course, textbooks, articles, internet resources Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Topics covered in the course, textbooks, articles, internet resources Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 15.05.2023 01:26