Title : Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment

This article, titled Prediction of Surface Urban Heat Island Based on Predicted Consequences of Urban Sprawl Using Deep Learning: A Way Forward for a Sustainable Environment, explores the impact of urban sprawl on the "Surface Urban Heat Island" (SUHI) phenomenon, using deep learning techniques to predict this effect. The study focuses on finding sustainable solutions to mitigate the negative environmental impacts of rapid urban expansion.
Details and Key Aspects of the Article:
1. Topic:
- The article analyzes the relationship between urban sprawl and the increase in surface temperatures in urban areas, known as the Surface Urban Heat Island effect. Urban sprawl refers to the rapid and often uncontrolled expansion of urban areas, which typically results in increased construction and reduced green spaces. These changes lead to higher heat absorption and retention by constructed surfaces, intensifying the urban heat island effect.
2. Research Objective:
- The main objective of this research is to develop a predictive model to simulate changes in surface temperatures in urban areas due to urban sprawl. These predictions are intended to assist urban planners and policymakers in making informed decisions to reduce SUHI effects, thereby supporting urban environmental sustainability.
3. Methodology:
- Data Collection: Temperature, vegetation cover, construction density, and other urban factors were collected from satellite imagery and land-based data.
- Data Processing and Preparation: The data was preprocessed using image processing techniques and normalization to prepare it for the deep learning model.
- Deep Learning Models: The study employed deep learning models to identify hidden patterns between urban sprawl and surface temperature changes. Convolutional Neural Networks (CNNs), known for their effectiveness in image processing, were used to recognize these patterns.
- Model Training and Validation: The model was trained using historical data and validated with recent data to assess its accuracy and predictive capability.
4. Results and Findings:
- The results indicate that the model can accurately predict the effects of urban sprawl on surface temperatures.
- Increased construction areas and reduced green spaces contribute to intensified SUHI effects, leading to greater energy demand for cooling buildings and increased environmental pollution.
- The study concludes that deep learning-based SUHI prediction can guide policy and planning efforts aimed at minimizing the adverse effects of urban sprawl.
5. Practical Applications and Recommendations:
- The research recommends that cities adopt sustainable development practices, such as increasing green spaces and using reflective building materials, to mitigate the urban heat island effect.
- Additionally, the proposed model can be used by urban planners as a tool for future planning and designing smart cities that account for environmental impacts.

In summary, this article highlights how deep learning can support sustainable urban planning by helping to predict and mitigate the negative impacts of urban sprawl, aiming for a more sustainable urban future.