Modeling Environmental Pollution Maps in the Erbil Plain Using Remote Sensing and Geospatial Artificial Intelligence (GeoAI)
DOI:
https://doi.org/10.25130/jfa.conf.10.1.11Keywords:
Erbil Plain, Environmental Pollution, Remote Sensing, GeoAI, NDVI, LST, NDBI, SOAbstract
This study aims to analyze the spatial distribution of environmental pollution in the Erbil Plain using remote sensing data and Geospatial Artificial Intelligence (GeoAI) techniques. Satellite imagery from Landsat and Sentinel was utilized to calculate key environmental indicators representing different ecosystem components, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Built-up Index (NDBI), as well as Sentinel-5P data for air pollutants such as sulfur dioxide (SO₂). Several GeoAI and spatial analysis methods were applied, including the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) algorithms, to classify pollution-affected areas and predict future patterns. The results revealed that integrating remote sensing with GeoAI significantly enhanced the accuracy of spatial pollution mapping, identifying high-pollution zones concentrated within urban and industrial centers of Erbil, whereas rural and vegetated areas showed lower pollution levels. The study recommends adopting smart environmental monitoring systems based on GeoAI technologies to support sustainable resource management and evidence-based environmental planning.
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