Enhanced Data Analysis from GIS as a Smart City by Machine Learning
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 1 |
| Year of Publication : 2025 |
| Authors : Ahmed Anwer Al Jumaili, Kifah Tout, Zaid F. Makki |
10.14445/23488549/IJECE-V12I1P107
How to Cite?
Ahmed Anwer Al Jumaili, Kifah Tout, Zaid F. Makki, "Enhanced Data Analysis from GIS as a Smart City by Machine Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 1, pp. 92-103, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I1P107
Abstract:
Smart cities have recently expanded and become a phenomenon sought by urbanized societies. This expansion increases the need for applications to manage these systems efficiently. In this study, we present an approach that integrates Geographic Information Systems (GIS) with one of the Machine Learning (ML) algorithms in order to enhance the analysis of important data that helps in developing smart cities, such as traffic, environmental monitoring, and resource allocation for making important decisions. The well-known classifier Support Vector Machines (SVM) help classify classes and recognize patterns, especially when adding weights that affect the features extracted from the data in the standard dataset. Due to the integration of Artificial Intelligence (AI) techniques with GIS, planning smart city infrastructure and predicting future trends in forecasting improved. Urban management in smart cities is more dynamic through the proposed approach. The study proved the worthiness of the proposed method through good results, as the prediction accuracy reached 90% and high results for the rest of the evaluation criteria. This research paves the way for taking advantage of artificial intelligence techniques by integrating them with GIS.
Keywords:
Smart city, Support Vector Machines (SVM), Geographic Information Systems (GIS), Machine Learning (ML), Prediction.
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