Application of Machine Learning Algorithms to Predict Urban Expansion: Fid-Bau Portal

Application of Machine Learning Algorithms to Predict Urban Expansion

Robi, Rejira K. / George, Jain K.

Urban expansion presents significant challenges for sustainable development. Predicting urban growth patterns is crucial for effective urban planning and resource management. This review explores the application of machine learning models in predicting urban expansion. This study aims to systematically review existing literature on using machine learning models for urban expansion prediction. We hypothesize that machine learning, particularly deep learning techniques, can offer valuable insights and improve the accuracy of urban growth predictions compared to traditional methods. A comprehensive literature search was conducted using relevant databases to identify research articles addressing urban expansion prediction with machine learning models. The search strategy included keywords related to urban growth, expansion, machine learning, and various model types. Inclusion and exclusion criteria were established to ensure the relevance and quality of the retrieved studies. Data extraction focused on the types of urban growth models, variables considered, machine learning methodologies employed, and the effectiveness of the models in predicting urban expansion. The review identified various types of machine learning models used for urban expansion prediction, including shallow learning (e.g., random forest, support vector machines) and deep learning (e.g., convolutional neural networks, long short-term memory) architectures. Studies analyzed the influence of diverse variables such as infrastructure, demographics, and environmental factors on urban growth patterns. The review found that deep learning models generally demonstrated superior performance to shallow learning models in predicting urban expansion due to their ability to handle complex spatial data and relationships. Case studies from China and Korea provided practical examples of applying these models and showcased their potential for real-world urban planning applications. This review highlights the growing potential of machine learning models, particularly deep learning, for predicting urban expansion. By incorporating various urban growth indicators and leveraging advanced learning algorithms, these models offer a data-driven approach for informed urban planning decisions. Further research is needed to explore the integration of explainability techniques within these models and their application in diverse geographic contexts.