Integrating Deep Learning, Geospatial Modeling, and Explainable AI for Urban Heat Risk Reduction in the United States

Abstract

The Urban Heat Island (UHIs) is an important environmental problem of rapidly developing cities, contributing to the exposure to heat, aggravating air quality, and enhancing the rate of health risks caused by climate changes. Since the process of urbanization all over the world has been growing evenly during the past decade, urban areas have gotten hotter in comparison to the surrounding land areas, which has resulted in the need to consume more energy, overburdening the infrastructure, and making the process of thermoregulation unpleasant. The most recent advancement of machine learning (ML) introduced the application of powerful analytical tools that can identify the presence of UHI trends, extreme heat events, and simplify the climate-resilient infrastructure policy (Zhou et al., 2019). The paper will also determine the application of ML-based models in the reduction of UHIs through the use of predictive intelligence, dynamically allocating resources, and planning cities based on data.
To predict the urban temperature data, the satellite-based land surfaces, and infrastructure vulnerability indicators, this article uses empirical data (2016 2020) that is back-dated to run the empirical data. Another hybrid algorithm- Urban Heat Island Neural Network ( UHINet ) is introduced and offered to educate spatial-temporal change of the temperature and propose certain mitigating actions, such as planting sites, reflective surfaces, and the best construction of buildings. The complementary geospatial models also are incorporated like Geo-Heat Mapping System (GHMS) and the Adaptive Environmental Heat Forecasting Model (AEHF) to supplement pattern recognition and complementary interpretability. It was discovered that UHINet has the potential to perform much better than classical algorithms and enhance the accuracy of prediction by 14.6 percent and decrease the mean temperature forecasting error by 22 percent in all datasets.
It is also stated in the research that ML can effectively measure effectiveness of mitigation measures with the help of statistical tools and formulas of heat-intensity. It has shown that green-infrastructure interventions delivered an average of 1.8 o C of urban cooling and 2.3 o C maximum surface temperature of local surfaces of high-albedo surface treatments (Li and Bou-Zeid, 2018). Such findings justify the radical application of machine learning in fostering the resilience of cities. With climate science, data analytics, and smart optimization models, the research will indicate a scalable solution, whereby cities can be in a position to adapt to the change in the heat stress and come up with climate-resilient infrastructure by the next few decades.