Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Forecasting the Scintillation Index Using Neural Networks

Al Ghezi, Mohammad Al Baqer
http://orcid.org/0009-0002-8419-7527
http://rave.ohiolink.edu/etdc/view?acc_num=dayton170248927622127

Abstract Details

2023, Master of Science (M.S.), University of Dayton, Electro-Optics.
This thesis objective is the forecasting of the scintillation index using a machine learning approach. The scintillation index is a measure of the fuctuations of optical wave intensity, also known as scintillation, occurring during the propagation through atmospheric turbulence. The data used for the machine learning-based scintillation index forecastign was obtained during on-going measurements conducted during several years over a 7 km propagation path at the Intelligence Optics Laboratory of the University of Dayton with a commercial scintillometer. Besides the scintillation index and refractive index structure parameter, also meteorological data such as air temperature, wind speed, and relative humidity were measured on both ends of the propagation path. To investigate the infuence of seasonal changes on the forecasting of the scintillation index, the data was divided into four subsets corresponding to the four seasons. Necessary data preprocessing steps have been performed, and the data was used to train diferent machine learning models. The considered models included: bi-directional long short-term memory (Bi-LSTM), convolutional neural network (CNN), K-nearest neighbor (KNN), and random forest (RF). Diferent Bi-LSTM models were trained by utilizing a single meteorological parameter as an input. Other Bi-LSTM models were trained on diferent pairs of meteorological parameters (i.e., air temperature and relative humidity, air temperature and wind speed, and relative humidity and wind speed), as well as using all meteorological parameters as inputs. Performance in scintillation index forecasting by different models was compared using a root mean squared error (RMSE). It was found that the Bi-LSTM model trained on all meteorological parameters demonstrated the best performance with RMSE = 1.274 in fall, 2.359 in winter, 4.317 in spring, and 1.700 in summer.
Miranda van Iersel (Advisor)
Grigorii Filimonov (Committee Member)
Thomas Weyrauch (Committee Member)
95 p.
Engineering; Optics
Scintillation index prediction; ANN; Meteorological Data; Atmospheric Optics; Machine Learning

Recommended Citations

Citations

  • Al Ghezi, M. A. B. (2023). Forecasting the Scintillation Index Using Neural Networks [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton170248927622127

    APA Style (7th edition)

  • Al Ghezi, Mohammad. Forecasting the Scintillation Index Using Neural Networks. 2023. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton170248927622127.

    MLA Style (8th edition)

  • Al Ghezi, Mohammad. "Forecasting the Scintillation Index Using Neural Networks." Master's thesis, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton170248927622127

    Chicago Manual of Style (17th edition)

dayton170248927622127
24
© 2023, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.