Machine Learning Predictors for Sustainable Urban Planning

Nagappan, Sarojini Devi; Daud, Salwani Mohd

doi:10.14569/ijacsa.2021.0120787

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…With the rapid development of AI technology, machine learning (ML) has attracted widespread attention from scholars for its powerful learning and computational capabilities that can enable intelligent analysis of data trends and patterns [39]. By selecting relevant samples and training the model to explore non-linear relationships between influencing factors and geographical phenomena, ML can efficiently clarify the importance of each indicator to the sample data while minimizing errors caused by subjective experience and obtaining more objective and scientific indicator weights [40]. At present, ML-based weight determination methods have been applied in the indicator construction and composite assessment for purposes such as evaluating performance, detecting spatial patterns, and selecting locations [41], [42], [43].…”

Section: Introductionmentioning

confidence: 99%

Integrating Multisource Geographic Big Data to Delineate Urban Growth Boundary: A Case Study of Changsha

Gao,

Xia,

Zhuang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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An urban growth boundary (UGB) is an important policy tool used to control urban sprawl, which can effectively balance the urban construction needs, residents' quality of life, and urban ecological protection. Current studies of UGB delineation and its indicators have paid little attention to human factors such as human activities and economic vitality, and weights for evaluation indicators have been determined highly subjectively. In response to these problems, this study integrated multi-source geographic big data to construct a total of 30 natural, human, and ecological evaluation indicators. The GIS technology and machine learning (ML) approach were combined to determine indicator weights with an officially manually drawn 2035 UGB as the reference, aiming to reduce the subjectivity. The suitability score was then calculated from indicator grading 4-1 and related weights, and high suitability (>2.15) regions were eventually delineated as the UGB. Results showed a delineated UGB of 1,528.06 km 2 with an overall accuracy of over 93% and high consistence with reference data for 2035 in the Chinese city of Changsha. The geographic big data totally contributed more than 33.72%, which mainly characterized role of human elements, and a 5-6 percentage point reduction in accuracy was found without these data. Compared with existing studies, our delineated UGB had higher accuracy and closer spatial pattern to the reference data, verifying the effectiveness and reasonability of ML-based weight setting approach and index system with geographic big data. The proposed method can provide scientific and accurate framework for UGB delineation, which can promote the territorial spatial planning and sustainable urban development.

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Section: Introductionmentioning

confidence: 99%

Integrating Multisource Geographic Big Data to Delineate Urban Growth Boundary: A Case Study of Changsha

Gao,

Xia,

Zhuang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Integrating machine learning in digital architecture: enhancing sustainable design and energy efficiency in urban environments

Abu-Shaikha,

Al-Karablieh,

Musa

et al. 2024

Asian J Civ Eng

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A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change

Forster,

Bindreiter,

Uhlhorn

et al. 2024

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The impacts on living conditions and natural habitats deriving from planning decisions require complex analysis of cross-acting factors, which in turn require interdisciplinary data. At the municipal level, both data collection and the knowledge needed to interpret it are often lacking. Additionally, climate change and species extinction demand rapid and effective policies in order to preserve soil resources for future generations. Ex-ante evaluation of planning measures is insufficient owing to a lack of data and linear models capable of simulating the impacts of complex systemic relationships. Integrating machine learning (ML) into systemic planning increases awareness of impacts by providing decision-makers with predictive analysis and risk mitigation tools. ML can predict future scenarios beyond rigid linear models, identifying patterns, trends, and correlations within complex systems and depicting hidden relationships. This article focuses on a case study of single-family houses in Upper Austria, chosen for its transferability to other regions. It critically reflects on an ML approach, linking data on past and current planning regulations and decisions to the physical environment. We create an inventory of categories of areas with different features to inform nature-based solutions and backcasting planning decisions and build a training dataset for ML models. Our model predicts the effects of planning decisions on soil sealing. We discuss how ML can support local planning by providing area assessments in soil sealing within the case study. The article presents a working approach to planning and demonstrates that more data is needed to achieve well-founded planning statements.

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Machine Learning Predictors for Sustainable Urban Planning

Cited by 4 publications

References 43 publications

Integrating Multisource Geographic Big Data to Delineate Urban Growth Boundary: A Case Study of Changsha

Integrating Multisource Geographic Big Data to Delineate Urban Growth Boundary: A Case Study of Changsha

Integrating machine learning in digital architecture: enhancing sustainable design and energy efficiency in urban environments

A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change

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