Multi-Model Analysis of Climatic Impacts on Urban Infrastructure Using Machine Learning and Statistical Methods

Urban infrastructure systems are increasingly vulnerable to the impacts of climate change, particularly in developing countries where resilience planning is limited. Cities across Nigeria, including Lagos, Abuja, Enugu, Maiduguri, Kano, and Port Harcourt, are experiencing significant climate stressors such as extreme rainfall and rising temperatures that contribute to flood events and infrastructure deterioration. Understanding how these climatic variables influence urban infrastructure is vital for proactive decision making and effective adaptation strategies.
This study presents a multi-model analysis that integrates machine learning and statistical techniques to evaluate the relationship between climate indicators and infrastructure performance across these six cities. Historical climate and infrastructure data from 2000 to 2024 were collected, processed, and analyzed. Exploratory data analysis and visualization were performed to understand variable relationships, followed by preprocessing such as scaling and encoding. Multiple regression models including linear regression, support vector regression, and multilayer perceptron were implemented using a pipeline framework to predict infrastructure conditions. Additionally, Ordinary Least Squares (OLS) regression was used for interpretability and statistical validation, including evaluation of multicollinearity using the Variance Inflation Factor (VIF).
The study found a strong correlation between rainfall patterns and flood events, significantly affecting infrastructure quality. Model evaluation revealed that machine learning methods offered higher predictive accuracy, while statistical models provided greater insight into variable significance. This combined approach bridges the gap between prediction and interpretation, supporting data-informed urban planning and policy making. The study contributes to the body of knowledge on climate-resilient infrastructure and provides a framework adaptable to other regions facing similar challenges.

Climate change, Urban infrastructure

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