Geo AI for Predicting Utility Asset Failures in Electric Networks: A Machine Learning Approach Using Geospatial and Environmental Data

Abstract

The paper discusses the research that explored the power of Geo AI for correctly predicting asset failures in any type of utility network. Electric utility, gas utility, or water utility is highly vulnerable to environmental distress such as heavy rain or rising temperature. Traditional reactive approaches of maintenance, inspection, and rectification lead to service disruptions, and in today’s day and age it is almost impossible to do something productive without a power supply. My research presents a machine learning-based approach for proactively predicting equipment failure by integrating spatial data analysis and machine learning. Study area is Odisha, which is very prone to cyclones, low depression, and rising temperatures. An electric file geodatabase was created using ArcGIS Pro and ArcPy in which real-world asset configuration was simulated. Environmental attributes like temperature and rainfall were also added using zonal statistics. This research shows how Geo AI and machine learning can be effectively used to derive preventive maintenance techniques in utility networks. The proposed project is easy to implement and cost-effective and can be viewed as a prototype for integrating predictive modeling into a GIS-based utility management system.

Introduction

The increasing vulnerability of electric utility networks—driven by rising energy demand, aging infrastructure, and extreme weather—has highlighted the limitations of traditional schedule-based maintenance, which lacks predictive and spatial intelligence. To address this, the study explores the use of GeoAI, an integration of Geographic Information Systems (GIS) and Artificial Intelligence (AI), for proactive utility asset failure prediction.

The research demonstrates a GeoAI-based framework that combines geospatial, environmental, and asset-related data with machine learning (ML) models to predict failures in electric utility components. A case study was conducted in Odisha, India, a region prone to cyclones, heatwaves, and heavy rainfall. Due to the unavailability of real utility failure data, a synthetic GIS-based utility network was created using ArcGIS Pro and ArcPy, consisting of electric lines, devices, and junctions with attributes such as asset type, age, rainfall, and temperature.

Four ML models—Random Forest, CatBoost, K-Nearest Neighbors, and Naïve Bayes—were trained and evaluated. Among them, Random Forest performed best, achieving the highest accuracy and overall classification performance. Feature importance analysis identified asset age, temperature, and rainfall as the most influential predictors of failure. The predicted results were visualized spatially in ArcGIS Pro, enabling identification of high-risk assets and supporting preventive maintenance planning.

The study’s key contributions include:

1. Development of a GeoAI-based predictive maintenance framework for electric utilities.

2. Integration of ArcPy-based spatial preprocessing with environmental and asset data.

3. Demonstration of a scalable, ML-driven risk model applicable to real-world utility networks.

Overall, the research confirms that integrating machine learning with geospatial and environmental data can effectively support proactive, risk-based maintenance of electric utility infrastructure, even when real-world failure datasets are unavailable.

Conclusion

This research presents a Geo AI based approach for predicting electric utility asset failures by integrating spatial data science, machine learning, and GIS technologies. Focusing on the Indian state of Odisha, we constructed a synthetic utility geodatabase enriched with realistic environmental attributes (rainfall and temperature) and trained multiple machine learning models to assess asset health.

References

[1] Esri. (2023). ArcGIS Pro SDK Documentation. Retrieved from: https://pro.arcgis.com [2] Zhou, Y., He, Y., & Zhang, H. (2021). Predictive Maintenance of Utility Assets Using Machine Learning and GIS. IEEE Access, 9, 114203–114215. https://doi.org/10.1109/ACCESS.2021.3105101 [3] Nassiry, D., (2020). Machine Learning in Predictive Maintenance: A Review of the State of the Art. Renewable and Sustainable Energy Reviews, 134, 110404. [4] Tao, Y., Liu, Y., & Zhang, X. (2020). GeoAI: Spatially Explicit Machine Learning for Environmental Applications. International Journal of Geographical Information Science, 34(3), 385–398. [5] CHIRPS Dataset. Climate Hazards Group. Retrieved from: https://www.chc.ucsb.edu/data/chirps [6] MODIS Land Surface Temperature (LST). NASA Earthdata. Retrieved from: https://modis.gsfc.nasa.gov [7] Pedregosa, F. et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. [8] Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., & Gulin, A. (2018). CatBoost: Unbiased [9] https://www.youtube.com/watch%3Fv%3DkRMOKSud9Lk

Copyright

Copyright © 2026 Bibhu Prasad Mishra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.