The Ma’rifa Hall at the Technical Engineering College – Baghdad witnessed the defense of the master’s thesis by the student Eilaf Abdul Sattar Abdullah, specializing in Geospatial Engineering Technologies, on the morning of Sunday, February 23, 2025. Her thesis is titled:
“Soil Salinity Mapping Using Sentinel-1 SAR, Sentinel-2 MSI, and Advanced Machine Learning Algorithms: A Case Study of Diyala Province”
Study Objective:
The study aims to produce soil salinity maps for Diyala Province for the year 2024 based on soil electrical conductivity values, remote sensing data, and spectral indices related to soil salinity analysis derived from the Sentinel-1 and Sentinel-2 satellites using Google Earth Engine.
Three machine learning algorithms—ANN (Artificial Neural Networks), SVM (Support Vector Machine), and RF (Random Forest)—were applied to predict soil electrical conductivity values. The SVM algorithm outperformed RF and ANN in terms of performance, achieving the lowest RMSE and MAE values and the highest correlation coefficient.
The predicted soil electrical conductivity values generated by the SVM algorithm were used to create a soil salinity map using the ArcMap GIS 10.8 software tools.
Thesis Defense Committee:
- Salem Harez Jassam / Institute of Trainers Preparation / Chair
- Prof. Dr. Jasim Ahmed Ali / Technical Engineering College – Baghdad / Member
- Prof. Youssef Hussain Khalaf / University of Baghdad / Member
- Karim Hassan Alwan / University of Baghdad / Supervisor
- Prof. Dr. Ahmed Hussain Hamdallah / Technical Engineering College – Baghdad / Supervisor
After extensive discussion of the thesis and consideration of the scientific observations, the thesis was successfully accepted.
