Prediction of the Dead Sea water level using neural networks
Other Title(s)
التنبؤ بمستوى سطح البحر الميت باستخدام الشبكات العصبونية
Dissertant
Shambur, Muhammad Khalid Yusuf Muhammad
Thesis advisor
Comitee Members
al-Hindi, Khalid M.
Fadl, Muayyad A.
University
Philadelphia University
Faculty
Faculty of Information Technology
Department
Department of Computer Science
University Country
Jordan
Degree
Master
Degree Date
2008
English Abstract
-The Dead Sea (DS) basin plays a major role for regional economic development (industry, tourism and agriculture) in Jordan.
Different studies stated that the water level of the DS is dropping an average of 3 feet per year.
Accordingly there is a need to provide accurate and reliable estimates for the water level to help the researchers and geologists of the DS to make different kind of studies giving results, so they can understand the state of the DS and its behavior and stop the dropping of the DS water level.
Neural Networks (NN) are computational models with the capacity to learn, to generalize, or to organize data based on parallel processing.
Among all kinds of networks, the most widely used are BackPropagation (BP), Levenberg-Marquardt (L-M), and Generalized Regression Neural Networks (GRNN) that are capable of representing non-linear functional mappings between inputs and outputs.
Different NN based DS water level prediction models are built and compared to determine the most effective neural networks work in prediction.
It is known that DS water level depends on many factors such as Air temperature, Salinity, Humidity and other environmental information.
Our NN models capture different subsets of those effects, reflect them within our models to identify the most effective set, which has significant impact on the water level of DS.
Finally, we can say that the proposed GRNN model provides best significant performance results comparing with other NN models using Mean Square Error (MSE).
Main Subjects
Earth Sciences, Water and Environment
Information Technology and Computer Science
Topics
No. of Pages
64
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Artificial neural networks.
Chapter Four : Design and implementation.
Chapter Five : Experimental results.
Chapter Six : Conclusions and future work.
References.
American Psychological Association (APA)
Shambur, Muhammad Khalid Yusuf Muhammad. (2008). Prediction of the Dead Sea water level using neural networks. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-549042
Modern Language Association (MLA)
Shambur, Muhammad Khalid Yusuf Muhammad. Prediction of the Dead Sea water level using neural networks. (Master's theses Theses and Dissertations Master). Philadelphia University. (2008).
https://search.emarefa.net/detail/BIM-549042
American Medical Association (AMA)
Shambur, Muhammad Khalid Yusuf Muhammad. (2008). Prediction of the Dead Sea water level using neural networks. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-549042
Language
English
Data Type
Arab Theses
Record ID
BIM-549042
