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Course Information
Course Unit Title : Artificial Neural Networks Applications in Earth Sciences
Course Unit Code : 01JEO9618
Type of Course Unit : Optional
Level of Course Unit : Second Cycle
Year of Study : Preb
Semester : 255.Semester
Number of ECTS Credits Allocated : 6,00
Name of Lecturer(s) : ---
Course Assistants : ---
Learning Outcomes of The Course Unit : To learn the basis of artificial neural networks
To improve model with Artificial neural networks method
To apply artificial neural networks methods in earth sciences problems
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : Introduction to artificial neural networks. Multi-layer artificial neural networks, training algorithms, modeling principles and engineering applications. Radial-based artificial neural networks, multi-layered artificial neural networks and applications according to their strengths and shortcomings. Generalized regression neural networks, artificial neural networks applications in hydrology, artificial neural networks applications in hydrogeology, artificial neural networks applications in engineering geology.
Languages of Instruction : Turkish-English
Course Goals : To provide understanding of basic principles related to ANN applications
Course Aims : To teach artificial neural networks (ANN) applications in Earth Sciences problems
WorkPlacement  
Recommended or Required Reading
Textbook : Artificial Neural Networks Applications in Earth Sciences lecture notes (prepared by Şehnaz Şener)
Additional Resources : ? Yapay Sinir Ağları İlkeleri / Zekai Şen, Su Vakfı Yayınları ? Yapay Sinir Ağları / Ercan Öztemel, Papatya Yayıncılık
Material Sharing
Documents :
Assignments :
Exams :
Additional Material :
Planned Learning Activities and Teaching Methods
Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
ECTS / Table Of Workload (Number of ECTS credits allocated)
Student workload surveys utilized to determine ECTS credits.
Activity :
Number Duration Total  
Course Duration (Excluding Exam Week) :
3 14 42  
Time Of Studying Out Of Class :
3 14 42  
Homeworks :
4 6 24  
Presentation :
0 0 0  
Project :
0 0 0  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 25 25  
Finals :
1 45 45  
Workload Hour (30) :
30  
Total Work Charge / Hour :
178  
Course's ECTS Credit :
6      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 60
Quiz :
0 0
Homework :
1 40
Attendance :
0 0
Application :
0 0
Lab :
0 0
Project :
0 0
Workshop :
0 0
Seminary :
0 0
Field study :
0 0
   
TOTAL :
100
The ratio of the term to success :
40
The ratio of final to success :
60
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 Introduction to artificial neural networks
  Study Materials: Lecture notes and related references indicated in additional reseources
2 Multi-layer artificial neural networks
  Study Materials: Lecture notes and related references indicated in additional reseources
3 Training algorithms
  Study Materials: Lecture notes and related references indicated in additional reseources
4 Modeling principles and engineering applications
  Study Materials: Lecture notes and related references indicated in additional reseources
5 Radial-based artificial neural networks
  Study Materials: Lecture notes and related references indicated in additional reseources
6 Multi-layered artificial neural networks and applications according to their strengths and shortcomings
  Study Materials: Lecture notes and related references indicated in additional reseources
7 Generalized regression neural networks
  Study Materials: Lecture notes and related references indicated in additional reseources
8 Examples of model development
  Study Materials: Lecture notes and related references indicated in additional reseources
9 Artificial neural networks applications in hydrology,
  Study Materials: Lecture notes and related references indicated in additional reseources
10 Applications of artificial neural networks in modeling of the groundwater level
  Study Materials: Lecture notes and related references indicated in additional reseources
11 Applications of artificial neural networks in water quality modeling
  Study Materials: Lecture notes and related references indicated in additional reseources
12 Applications of artificial neural networks in landfill site selection problems
  Study Materials: Lecture notes and related references indicated in additional reseources
13 Artificial neural networks applications in engineering geology.
  Study Materials: Lecture notes and related references indicated in additional reseources
14 General assessment
  Study Materials: Lecture notes and related references indicated in additional reseources