jaza, S., baqi, T., panah, H. (2025). Wetland Areas Trend and Examining Effective Factors with Machine Learning. , 16(4), 282-294. doi: 10.58928/ku25.16430
srwsht muhammed jaza; tarza jabar baqi; hiwa faraj panah. "Wetland Areas Trend and Examining Effective Factors with Machine Learning". , 16, 4, 2025, 282-294. doi: 10.58928/ku25.16430
jaza, S., baqi, T., panah, H. (2025). 'Wetland Areas Trend and Examining Effective Factors with Machine Learning', , 16(4), pp. 282-294. doi: 10.58928/ku25.16430
jaza, S., baqi, T., panah, H. Wetland Areas Trend and Examining Effective Factors with Machine Learning. , 2025; 16(4): 282-294. doi: 10.58928/ku25.16430

Wetland Areas Trend and Examining Effective Factors with Machine Learning

Kirkuk University Journal for Agricultural Sciences (KUJAS)
Volume 16, Issue 4, December 2025, Pages 282-294    PDF (805.25 K)
Document Type: Research Paper
DOI: 10.58928/ku25.16430
Authors
srwsht muhammed jaza* 1; tarza jabar baqi2; hiwa faraj panah3
1Iraqi
2Surveying Department,Darbandikhan technical institute,sulaimani polytechnic university,sulaimani,KGR,Iraq
3Civil Engineering and Architecture Faculty, Shahid Chamran University of Ahvaz, Ahvaz
Abstract
Wetlands are special ecosystems providing crucial hydrological, ecological, and socio-economic services. The study investigates the long-term development of the Hammar Marsh in Iraq from 2000 to 2025, focusing on water level trends and the driving environmental forces of the changes. Remote sensing imagery is analyzed using Google Earth Engine to obtain monthly water surface areas and other key climatic and ecological variables. Mann–Kendall test and Sen's slope estimator were applied to detect significant trends in water level, and there was an overall increase, with summer and autumn being particularly so, while winter and early spring had slower changes.

Stepwise Variance Inflation Factor (VIF) analysis was performed to reduce multicollinearity among predictors so that all remaining variables had VIF values below 10. A Random Forest model was then executed to infer the relative importance of environmental drivers. The model exhibited test set R² of 0.690 and RMSE of 0.154, indicating good predictability. Calculation of the variable importance indicated that the Palmer Drought Severity Index (PDSI) and soil moisture were the dominant controlling factors of water level change, followed by vegetation cover (NDVI) and land surface temperature (LST), with the other variables of precipitation, vapor pressure, wind speed, runoff, and aerosol optical depth having secondary effects.

The results highlight the synergistic effects of climatic and hydrological drivers on wetland dynamics and demonstrate the effectiveness of integrating remote sensing, trend analysis, and machine learning for wetland monitoring. The outcomes of this study have significant implications for the sustainable management and conservation of Hammar Marsh and other similar wetland ecosystems in the face of changing environmental conditions.
Keywords
Wetlands; Remote Sensing; Hydro-climatic Drivers; Hammar marsh; google earth engine(GEE); Random Forest Model
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