Integrating Socio-Political Dynamics and Geospatial Machine Learning: An Integrated-Hybrid ANN approach for sustainable Urban Planning in Post-Conflict Baghdad

Abstract

Rapid, post-conflict urbanization in Baghdad presents acute socio-environmental and infrastructure challenges that conventional remote-sensing models struggle to capture. This study develops a hybrid geospatial–socio-political framework that integrates high-resolution Landsat/Sentinel imagery and spatial indicators (Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Nighttime Lights (NTL), Population Density, Digital Elevation Model (DEM), Distance-to-Road/Water, Building Footprints) with socio-political rasters (UNHCR displacement statistics; subnational governance indices from the Global Data Lab) to forecast land-use/land-cover (LULC) to 2050. A multi-layer perceptron Artificial neural networks (ANN) (input = 9 predictors; hidden layers = 64–128–64; ReLU + dropout 0.3; softmax output) was trained on stratified samples (≈50,000 pixels/city) and implemented in Keras. Historical analysis (1990–2020) shows Baghdad’s built-up area rose ≈82% with mean NDVI declining ≈40%, while Riyadh’s built-up rose ≈55% with NDVI declining ≈20%. The ANN achieved ~88% overall accuracy and a Kappa of 0.82 on the test set. Projections to 2050 (medium-trend scenario) indicate further built-up increases of ≈25% for Baghdad and ≈15% for Riyadh. Feature-importance and ablation tests attribute the largest predictive contribution to displacement density (permutation accuracy drop ≈10.4%), followed by NDVI (≈8.8%) and governance indices (≈7.2%). Scenario-based sensitivity (±25% socio-political perturbations) alters Baghdad’s projected built-up share by ≈8 percentage points, underscoring high socio-political sensitivity; input extrapolation and sensor inter-calibration introduce additional uncertainty (assessed at ~±15–25% across inputs). The results argue for policy responses combining slum-upgrading, adaptive zoning, institutional strengthening, and real-time monitoring (IoT/NTL integration). Future work should apply explainable-AI methods, finer-scale socio-political data, and dynamic (feedback) models to improve causal interpretation and scenario planning.

Keywords

• Urban sprawl modeling
• Post-conflict planning
• Remote sensing analytics
• Artificial neural networks
• Informal settlements.

References

1. Al-Hameedi, W. M. M., Chen, J., Faichia, C., Al-Shaibah, B., Nath, B., Kafy, A.-A., Hu, G., & Al-Aizari, A. (2021). Remote sensing-based urban sprawl modeling using multilayer perceptron neural network Markov chain in Baghdad, Iraq. Remote Sensing, 13(20), 4034.
2. Al-Hathloul, S., & Mughal, M. A. (2004). Urban growth management-the Saudi experience. Habitat International, 28(4), 609-623. https://doi.org/https://doi.org/10.1016/j.habitatint.2003.10.009
3. Aljaddani, A. H. (2022). The land use history, economic drivers, and future trends of urban growth in Saudi Arabia.
4. Aryal, J., Sitaula, C., & Frery, A. C. (2023). Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia. Scientific reports, 13(1), 13510.
5. Avcı, C., Budak, M., Yağmur, N., & Balçık, F. (2023). Comparison between random forest and support vector machine algorithms for LULC classification. International Journal of Engineering and Geosciences, 8(1), 1-10.
6. Bag, A., Sharma, A., & Pal, S. (2024). Studying urbanization pattern in Sambalpur City during 1992-2042 using CA-ANN, and Markov-Chain model. International Journal of Engineering and Geosciences, 9(3), 356-367.
7. Canton, H. (2021). United nations human settlements programme—UN-habitat. In The Europa Directory of International Organizations 2021 (pp. 234-240). Routledge.
8. Chakraborty, T., Venter, Z. S., Demuzere, M., Zhan, W., Gao, J., Zhao, L., & Qian, Y. (2024). Large disagreements in estimates of urban land across scales and their implications. Nature communications, 15(1), 9165.

9. Dalal, S. (2023). The Role of United Nations in Economic and Social Development of Iraq Between 2003-2021.
10. Dolui, S., & Chakraborty, S. (2023). Forecasting the Effects of Urban Expansion on the Agricultural and Forest Landscape Using MLPNN-Markov Chain Model in Greater Guwahati Metropolitan Area, India. In Agriculture and Climatic Issues in South Asia (pp. 278-312). CRC Press.
11. Efe, E., & Algancı, U. (2023). Çok zamanlı Sentinel 2 uydu görüntüleri ve makine öğrenmesi tabanlı algoritmalar ile arazi örtüsü değişiminin belirlenmesi. Geomatik, 8(1), 27-34. , Elvidge, C. D., Baugh, K. E., Zhizhin, M., & Hsu, F.-C. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. Proceedings of the Asia-Pacific Advanced Network, 35(0), 62.
12. Fadhıl, A., & Kurban, T. (2022). Hücresel otomata markov zincir yöntemi ile kentsel yayılmanın modellenmesi: Kerkük ili örneği. Geomatik, 7(1), 58-70.
13. Gharaibeh, A. A., Jaradat, M. A., & Kanaan, L. M. (2023). A machine learning framework for assessing urban growth of cities and suitability analysis. Land, 12(1), 214.
14. Girma, Y., Terefe, H., Pauleit, S., & Kindu, M. (2019). Urban green infrastructure planning in Ethiopia: The case of emerging towns of Oromia special zone surrounding Finfinne. Journal of Urban Management, 8(1), 75-88. https://doi.org/https://doi.org/10.1016/j.jum.2018.09.004
15. Hanoon, S. K., Abdullah, A. F., Shafri, H. Z., & Wayayok, A. (2023). Urban growth forecast using machine learning algorithms and GIS-based novel techniques: a case study focusing on Nasiriyah City, Southern Iraq. ISPRS International Journal of Geo-Information, 12(2), 76.
16. Jamali, N. A., & Rahman, M. T. (2016). Utilization of remote sensing and GIS to examine urban growth in the city of Riyadh, Saudi Arabia. cities, 10(11).
17. Khan, A., & Sudheer, M. (2022). Machine learning-based monitoring and modeling for spatio-temporal urban growth of Islamabad. The Egyptian Journal of Remote Sensing and Space Science, 25(2), 541-550.
18. Komo, W., Sho, K., & Seta, F. (2025). Effectiveness of Riyad's Urban Growth Boundaries: An Evaluation Based on Building Permits, Vacancy, and Infrastructure Dynamics. Vacancy, and Infrastructure Dynamics.
19. Li, G., Fang, C., Li, Y., Wang, Z., Sun, S., He, S., Qi, W., Bao, C., Ma, H., & Fan, Y. (2022). Global impacts of future urban expansion on terrestrial vertebrate diversity. Nature communications, 13(1), 1628.
20. Mahendra, A., King, R., Du, J., Dasgupta, A., Beard, V. A., Kallergis, A., & Schalch, K. (2021). Seven transformations for more equitable and sustainable cities. World resources report, towards a more equal city.
21. Mansour, W., Maseeh, A. N., & Celiku, B. (2019). Iraq economic monitor, fall 2019: turning the corner-sustaining growth and creating opportunities for Iraq’s youth.
22. Mohammad, P., Goswami, A., Chauhan, S., & Nayak, S. (2022). Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India. Urban Climate, 42, 101116.
23. Morsy, S., & Hadı, M. (2022). Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt. International Journal of Engineering and Geosciences, 7(3), 272-282.
24. Moussa, Y. K., & Alwehab, A. A. (2022). The urban expansion impact on climate change for the city of Baghdad. Iraqi Journal of Science, 5072-5085.
25. Yakar, M., Yıldız, F., Uray, F., & Metin, A. (2010). Photogrammetric Measurement of The Meke Lake and Its Environment with Kite Photographs to Monitoring of Water Level to Climate Change. In ISPRS Commission V Mid-Term Symposium (pp. 613-616).
26. Mozumder, C., & Tripathi, N. K. (2014). Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network. International Journal of Applied Earth Observation and Geoinformation, 32, 92-104.
27. Nagappan, S. D., & Daud, S. M. (2021). Machine learning predictors for sustainable urban planning. International Journal of Advanced Computer Science and Applications, 12(7).
28. Unel, F. B., Kusak, L., & Yakar, M. (2023). GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal: GeoValueIndex. Aestimum, 82, 51-69..
29. Riad, P., Graefe, S., Hussein, H., & Buerkert, A. (2020). Landscape transformation processes in two large and two small cities in Egypt and Jordan over the last five decades using remote sensing data. Landscape and Urban Planning, 197, 103766.
30. Roy, S., Bose, A., Singha, N., Basak, D., & Chowdhury, I. R. (2021). Urban waterlogging risk as an undervalued environmental challenge: An Integrated MCDA-GIS based modeling approach. Environmental Challenges, 4, 100194.
31. Tayyebi, A., Pijanowski, B. C., & Tayyebi, A. H. (2011). An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran. Landscape and Urban Planning, 100(1-2), 35-44.
32. Tikoudis, I., Farrow, K., Mebiame, R. M., & Oueslati, W. (2022). Beyond average population density: Measuring sprawl with density-allocation indicators. Land use policy, 112, 105832.
33. Varma, B., Naik, N., Chandrasekaran, K., Venkatesan, M., & Rajan, J. (2024). Forecasting Land-Use and Land-Cover Change Using Hybrid CNN–LSTM Model. IEEE Geoscience and Remote Sensing Letters, 21, 1-5. https://doi.org/10.1109/LGRS.2024.3389671
34. Yu, D., & Fang, C. (2023). Urban remote sensing with spatial big data: A review and renewed perspective of urban studies in recent decades. Remote Sensing, 15(5), 1307.