Integration of Remote Sensing and Artificial Intelligence in Detecting the Environmental Changes of Najaf Sea in Iraq Using NDWI and GIS

Huda Jamal Jumaah; Nawal Kamal Khursheed; Veyan Farhad Salahalden; Maryam Hassan Ahmed Sulyman

doi:10.48084/etasr.12265

Authors

Huda Jamal Jumaah Department of Environment and Pollution Engineering, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq https://orcid.org/0000-0002-3438-3033
Nawal Kamal Khursheed Department of Environment and Pollution Engineering, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq
Veyan Farhad Salahalden Department of Surveying Engineering, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq
Maryam Hassan Ahmed Sulyman Department of Environment and Pollution Engineering, Technical Engineering College of Kirkuk, Northern Technical University, Kirkuk, 36001, Iraq

Volume: 15 | Issue: 4 | Pages: 26107-26112 | August 2025 | https://doi.org/10.48084/etasr.12265

Received: 20 May 2025 | Revised: 20 June 2025 | Accepted: 28 June 2025 | Online: 2 August 2025

Corresponding author: Huda Jamal Jumaah

Abstract

The Najaf Sea is undergoing significant environmental changes due to the climate change and urban expansion, resulting in decreasing water levels that affect the surrounding areas. The present study employs two technologies, Remote Sensing (RS) and Artificial Intelligence (AI), to monitor the environmental changes as part of the sustainability assessments. It aims to observe and analyze the changes between 2018 and 2025, tracking the variations in the water bodies and evaluating the impacts of the climate and human activity on sustainable resource management. Additionally, this supports rational decision-making regarding the environmental conservation, sustainable development, and resource management in the region. The Normalized Difference Water Index (NDWI), Support Vector Machine (SVM), Geographic Information Systems (GIS), and Sentinel-2 images were utilized to analyze the changes in the water bodies and their associated environmental impacts. Through change detection, AI models deliver highly accurate predictions of sustainable water resources. This paper highlights the critical role of advanced technologies in monitoring and forecasting the changes to water resources. The findings promote ecological protection and help local communities adapt to the environmental shifts through data-driven management. This research provides essential information to help experts develop resilient strategies for environmental conservation and climate change mitigation. As demonstrated, these technologies enhance monitoring in sensitive areas, such as the Najaf Sea. The study fosters ecological balance, protects the environment, and encourages development through technological advancements for the benefit of the local population.

Keywords:

AI, SVM, NDWI, remote sensing, GIS

Downloads

Download data is not yet available.

References

H. Kareem, "Study of water resources by using 3d groundwater modelling in Al-Najaf Region, Iraq," Ph.D. dissertation, School of Engineering, Cardiff University, UK, 2018.

R. M. Briant et al., "Disentangling late quaternary fluvial and climatic drivers of palaeohydrological change in the Najaf Sea basin, Western Iraq," Earth Surface Processes and Landforms, vol. 49, no. 4, pp. 1451–1467, 2024. DOI: https://doi.org/10.1002/esp.5779

J. H. Azzawi and I. M.-A. Abdulameer, "Environmental monitoring of water area in the Najaf sea, Iraq during 1990-2020 using remote sensing data," AIP Conference Proceedings, vol. 2437, no. 1, Aug. 2022, Art. no. 020034. DOI: https://doi.org/10.1063/5.0093818

S. M. Almudhafar, R. A. Mohsen, and B. A. Almayahi, "Environmental Assessment of the Impact of Water Pollution in the Bahar Al Najaf on Plants," Journal of Chemical Health Risks, vol. 13, no. 3, pp. 1036–1046, Oct. 2023.

A.S. Mahdi and A.A. Kzar, "Euphrates River Prediction Using Remote Sensing Techniques for Sustainable Development in Al-Najaf Al-Ashraf, Iraq," Library Progress – International: Library Science, Information Technology & Computer, vol. 44, no. 2s, July 2024, Art. no 446.

M. A. U. Sunny, "Unveiling spatial insights: navigating the parameters of dynamic Geographic Information Systems (GIS) analysis," International Journal of Science and Research Archive, vol. 11, no. 2, pp. 1976–1985, 2024. DOI: https://doi.org/10.30574/ijsra.2024.11.2.0690

S. K. McFeeter, "The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features," International Journal of Remote Sensing, vol. 17, no. 7, pp. 1425–1432, May 1996. DOI: https://doi.org/10.1080/01431169608948714

M. R. Mahmood, B. I. Abrahem, H. J. Jumaah, H. A. Alalwan, and M. M. Mohammed, "Drought monitoring of large lakes in Iraq using remote sensing images and normalized difference water index (NDWI)," Results in Engineering, vol. 25, Mar. 2025, Art. no. 103854. DOI: https://doi.org/10.1016/j.rineng.2024.103854

O. A. R. Yousef and H. S. Jaber, "Studying the Environmental Changes Using Remote Sensing and GIS," Iraqi Journal of Science, pp. 4605–4616, Jul. 2023. DOI: https://doi.org/10.24996/ijs.2023.64.7.45

J. Xie, J. Bu, H. Li, and Q. Wang, "Spaceborne GNSS Reflectometry for Vegetation and Inland Water Monitoring: Progress, Challenges, Opportunities, and Potential," Remote Sensing, vol. 17, no. 7, Jan. 2025, Art. no. 1199. DOI: https://doi.org/10.3390/rs17071199

J. Transon, R. D’Andrimont, A. Maugnard, and P. Defourny, "Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context," Remote Sensing, vol. 10, no. 2, Jan. 2018, Art. no. 157. DOI: https://doi.org/10.3390/rs10020157

F. Granata and F. Di Nunno, "Pathways for Hydrological Resilience: Strategies for Adaptation in a Changing Climate," Earth Systems and Environment, pp. 1–29, Jan. 2025. DOI: https://doi.org/10.1007/s41748-024-00567-x

S. Azizi, "Drought and Environmental Transformations of Lake Urmia: An Integrated Analysis Using Machine Learning and GIS," European Journal of Applied Sciences, vol. 12, no. 5, pp. 290–315, Oct. 2024. DOI: https://doi.org/10.14738/aivp.125.17718

D. N. Lerner and B. Harris, "The relationship between land use and groundwater resources and quality," Land Use Policy, vol. 26, pp. S265–S273, Dec. 2009. DOI: https://doi.org/10.1016/j.landusepol.2009.09.005

R. L. Singh and P. K. Singh, "Global Environmental Problems," in Principles and Applications of Environmental Biotechnology for a Sustainable Future, R. L. Singh, Ed. Singapore: Springer, 2017, pp. 13–41. DOI: https://doi.org/10.1007/978-981-10-1866-4_2

H. J. Jumaah, B. Kalantar, N. Ueda, O. S. Sani, Q. M. Ajaj, and S. J. Jumaah, "The Effect of War on Land Use Dynamics in Mosul Iraq Using Remote Sensing and GIS Techniques," in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Jul. 2021, pp. 6476–6479. DOI: https://doi.org/10.1109/IGARSS47720.2021.9553165

Y. Himeur, B. Rimal, A. Tiwary, and A. Amira, "Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives," Information Fusion, vol. 86–87, pp. 44–75, Oct. 2022. DOI: https://doi.org/10.1016/j.inffus.2022.06.003

H. J. Jumaah, M. H. Ameen, G. H. Mohamed, and Q. M. Ajaj, "Monitoring and evaluation Al-Razzaza lake changes in Iraq using GIS and remote sensing technology," The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 1, pp. 313–321, Feb. 2022. DOI: https://doi.org/10.1016/j.ejrs.2022.01.013

L. Cai and Y. Zheng, "Artificial intelligence-based automatic identification and classification of diverse sports using advanced deep learning models," International Journal of Information and Communication Technology, vol. 26, no. 23, pp. 91–113, May 2025. DOI: https://doi.org/10.1504/IJICT.2025.10071786

Z. T. Al-Sharify et al., "Using GIS model to investigate sustainable irrigation techniques from wastewater," AIP Conference Proceedings, vol. 2787, no. 1, Jul. 2023, Art. no. 090030. DOI: https://doi.org/10.1063/5.0150147

S. R. Krishnan et al., "Smart Water Resource Management Using Artificial Intelligence—A Review," Sustainability, vol. 14, no. 20, Oct. 2022, Art. no. 13384. DOI: https://doi.org/10.3390/su142013384

A. M. Hadi et al., "GIS-Based Rainfall Analysis using Remotely Sensed Data in Kirkuk Province, Iraq: Rainfall Analysis," Tikrit Journal of Engineering Sciences, vol. 29, no. 4, pp. 48–55, Dec. 2022. DOI: https://doi.org/10.25130/tjes.29.4.6

V. F. Terán-Gómez, A. M. Buitrago-Ramírez, A. F. Echeverri-Sánchez, A. Figueroa-Casas, and J. A. Benavides-Bolaños, "Integrating AHP and GIS for Sustainable Surface Water Planning: Identifying Vulnerability to Agricultural Diffuse Pollution in the Guachal River Watershed," Sustainability, vol. 17, no. 9, Jan. 2025, Art. no. 4130. DOI: https://doi.org/10.3390/su17094130

S. Qasim Hasan, "Shallow Model and Deep Learning Model for Features Extraction of Images," NTU Journal of Engineering and Technology, vol. 2, no. 3, pp.. 1-8, Nov. 2023. DOI: https://doi.org/10.56286/ntujet.v2i3.449

H. H. Kareem and A. A. Alkatib, "Determination the optimal prediction scenario for the future precipitation rates in the Al-Najaf Governorate in Iraq," International Journal of Hydrology Science and Technology, vol. 16, no. 2, pp. 171–186, Jan. 2023. DOI: https://doi.org/10.1504/IJHST.2023.132583

R. A.-H. Alkaitab, S. M. Almudhafar, and B. A. Almayahi, "An environmental analysis of the human potential to invest in alternative energy in Najaf Governorate," International journal of health sciences, vol. 6, no. S5, pp. 9026–9043, Jul. 2022. DOI: https://doi.org/10.53730/ijhs.v6nS5.11315

M. Al-Dabbas, "Water Resources of Iraq: An Overview," in The Geography of Iraq, S. M. Awadh and M. Al-Dabbas, Eds. Cham: Springer Nature Switzerland, 2024, pp. 49–81. DOI: https://doi.org/10.1007/978-3-031-71356-9_3

E. R. Wdaah and A. F. Nori, "Detection of Land Cover Change in the Sea of Najaf / Iraq for the Period (2005-2022) using Remote Sensing Techniques," International Journal of Advanced Multidisciplinary Research and Studies, vol. 3, no. 2, pp. 1201–1206, Oct. 2023.

D. İspir, "Spatiotemporal monitoring of the surface water level dynamics in Lake Tuz Türkiye using remote sensing and GIS," DYSONA - Applied Science, vol. 6, no. 2, pp. 378-388, Jul. 2025.

"NASA POWER | Data Access Viewer (DAV)." https://power.larc.nasa.gov/data-access-viewer/.

Z. Cui, S. Chen, B. Hu, N. Wang, C. Feng, and J. Peng, "Mapping Soil Organic Carbon by Integrating Time-Series Sentinel-2 Data, Environmental Covariates and Multiple Ensemble Models," Sensors, vol. 25, no. 7, Jan. 2025, Art. no. 2184. DOI: https://doi.org/10.3390/s25072184

D. A. Dzulhijjah, Kusrini, and R. Martinez-Bejar, "Comparative Analysis of Machine Learning Models for Sentinel-2 based Classification of the Bornean Heath Forest," Engineering, Technology & Applied Science Research, vol. 15, no. 2, pp. 21937–21943, Apr. 2025. DOI: https://doi.org/10.48084/etasr.10173

T. L. Dammalage and N. T. Jayasinghe, "Land-Use Change and Its Impact on Urban Flooding: A Case Study on Colombo District Flood on May 2016," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3887–3891, Apr. 2019. DOI: https://doi.org/10.48084/etasr.2578

R. Alsaeed, A. Aldarwish, L. Khouri, and V. Kolluru, "Response surface modeling of sodium hypochlorite-based manganese oxidation in drinking water," DYSONA - Applied Science, vol. 6, no. 2, Jul. 2025.