Artificial neural network for multifunctional areas

Area of Science:

• Environmental Science
• Urban Planning
• Geographic Information Systems

Background:

• Urban areas face challenges balancing environmental protection with anthropogenic development for sustainable growth.
• Territorial planning in densely populated regions requires careful consideration of land use changes and their impacts.
• Multifunctional areas possess unique socioeconomic and environmental value, necessitating protection during development.

Purpose of the Study:

• To mathematically simulate land use changes driven by urban expansion.
• To analyze the potential impact of increasing urban areas on valuable multifunctional zones.
• To inform territorial planning strategies for sustainable development in unique landscapes.

Main Methods:

• Implementation of an artificial neural network (ANN) model for land use simulation.
• Mathematical modeling to predict urban development patterns.
• Case study application in the Chianti Area, Florence, Italy.

Main Results:

• The ANN model successfully simulated potential urban land use changes.
• The study identified potential impacts of urban growth on multifunctional areas.
• Simulations provided insights into the dynamics of land use in a sensitive region.

Conclusions:

• Artificial neural networks offer a valuable tool for simulating and assessing the impacts of urban development on land use.
• Effective territorial planning is crucial for mitigating negative impacts on multifunctional areas and ensuring sustainable growth.
• The findings contribute to a better understanding of land use dynamics in culturally and environmentally significant regions like Chianti.