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Integrating Socio-Political Dynamics and Geospatial Machine Learning: An Integrated-Hybrid ANN approach for sustainable Urban Planning in Post-Conflict Baghdad
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DOI：10.26833/ijeg.1730367.png)
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Abstract
En 中文
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 (approximate to 50,000 pixels/city) and implemented in Keras. Historical analysis (1990-2020) shows Baghdad's built-up area rose approximate to 82% with mean NDVI declining approximate to 40%, while Riyadh's built-up rose approximate to 55% with NDVI declining approximate to 20%. The ANN achieved similar to 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 approximate to 25% for Baghdad and approximate to 15% for Riyadh. Feature-importance and ablation tests attribute the largest followed by NDVI (approximate to 8.8%) and governance indices (approximate to 7.2%). Scenario-based sensitivity (+/- 25% socio-political perturbations) alters Baghdad's projected built-up share by approximate to 8 percentage points, underscoring high socio-political sensitivity; input extrapolation and sensor intercalibration introduce additional uncertainty (assessed at similar to +/- 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
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348
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