Integrating GIS and AI for Solar Farm Site Selection: A Case Study of Nineveh Governorate– Iraq

   Solar energy is growing rapidly due to its abundance, availability, cleanliness, cost-effectiveness, and ease of installation. The site selection process plays a crucial role in maximizing the efficiency of solar projects while minimizing the environmental impact. This study focuses on Nineveh Governorate – Iraq, (which faces a power deficiency of 36%)— by introducing an advanced solar project site selection methodology, using the Geographic Information System (GIS) and Artificial Intelligence (AI). For this aim, seven machine learning (ML) algorithms namely, Logistic Regression, Averaged Perceptron, Boosted Decision Tree, Decision Forest, Support Vector Machine, Neural Network, and K-Means- were applied on Microsoft Azure ML to identify a suitable site for solar farms, Fourteen factors were identified that influenced the solar farm site included solar radiation, sandstorms, land surface temperature, Main Road, population, power lines, substation, land ownership, land cover, water resources, slop, and aspect. This factor was then categorized into four groups: environmental, climatic, topological, and socio-economic. Then the dataset was analyzed in Azure Machine Learning, using unsupervised and supervised ML. As a result, the Boosted Decision Tree model achieved the highest accuracy at 94.2%. The output results indicate that 27% of the study area is highly suitable for solar farm development. This research underlines the immense potential of the Nineveh Governorate for renewable energy projects. It creates a replicable framework linking GIS and ML to planning energy needs with minimal environmental impacts.