Understanding the Key Influencing Factors of Urban Resilience in Zhengzhou City, China: Quantification Using Spatial Machine Learning and the Density-Structure-Function Framework DOI

Tianshun Gu,

Hongbo Zhao, Yue Li

et al.

Published: Jan. 1, 2024

Language: Английский

Machine Learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments DOI

Ghazaleh Tanoori,

Alì Soltani,

Atoosa Modiri

et al.

Urban Climate, Journal Year: 2024, Volume and Issue: 55, P. 101962 - 101962

Published: May 1, 2024

Language: Английский

Citations

36

Evaluating the ecological security of ecotourism in protected area based on the DPSIR model DOI Creative Commons
Parvaneh Sobhani, Hassan Esmaeilzadeh, Isabelle D. Wolf

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 155, P. 110957 - 110957

Published: Sept. 19, 2023

Evaluating the ecological security of ecotourism (EES) in protected areas is critical because these play a vital role protecting biodiversity and natural resources. This study evaluated EES status Central Alborz Protected Area (Northern Iran), based on Driver, Pressure, State, Impact, Response (DPSIR) model. We developed comprehensive list 59 indicators for DPSIR model employed an Analytical Network Process (ANP) to determine indicator weights harnessing opinion experts which are most influential. approach facilitated identification regions with highest vulnerability, notably northern western sectors our area along boundary between Tehran Mazandaran provinces. Here, mechanisms that drive change include activities, livestock overgrazing, uncontrolled physical economic extensive road highway development, land use cover changes. Indicators effective determining status, activities. conclude by discussing respond increasing threat Areas such as involvement government strategic integrated management. Our serves methodological blueprint how evaluate Areas.

Language: Английский

Citations

32

Determining and Quantifying Urban Sprawl Drivers: A Delphi-DANP Approach DOI Creative Commons
Alì Soltani, Parviz Azizi, Masoud Javadpoor

et al.

Land, Journal Year: 2025, Volume and Issue: 14(2), P. 311 - 311

Published: Feb. 2, 2025

Urban sprawl poses a significant and escalating challenge in developing countries, including Iran, leading to substantial transformations urban areas. Despite efforts manage spatial development, uncontrolled exerts considerable pressure on resources, infrastructure, the environment. This study aims identify quantify drivers of investigate their interrelationships within Iranian metropolises. To achieve this objective, employs mixed-method approach, commencing with review existing literature expert surveys based PESTEL analysis Delphi method. stage identified categorized 40 key (sub-factors) into six main categories (factors): political, economic, social, technological, environmental, legal. Subsequently, DEMATEL-based Analytic Network Process (DANP) method is utilized explore internal among factors sub-factors determine relative weights, offering deeper insights relationships importance. The findings reveal complex interplay legal driving Iran. Key include political fragmentation, economic competition, social preferences for suburban living, rural-to-urban migration, increasing housing demand, weak regulations, natural constraints, inadequate transportation impact technological advancements. Based these findings, recommends holistic approach sustainable development emphasizing need stakeholder engagement, participatory decision making, reforms, investments public infrastructure.

Language: Английский

Citations

1

Street network patterns for mitigating urban heat islands in arid climates DOI Creative Commons

Kimia Chenary,

Alì Soltani, Ayyoob Sharifi

et al.

International Journal of Digital Earth, Journal Year: 2023, Volume and Issue: 16(1), P. 3145 - 3161

Published: Aug. 10, 2023

This study explores the impact of street pattern measurements on urban heat islands (UHI) in arid climate Mashhad, Iran. The Landsat-8 top-of-the-atmosphere (TOA) brightness images from 2013 to 2021, average values normalized difference vegetation index (NDVI) and land surface temperature (LST) were calculated. Street measurements, including closeness-centrality, straightness, orientation, employed analyse patterns each district. results indicated that districts with higher straightness lower closeness-centrality exhibit cooler temperatures. Strong correlations observed between LST NDVI, local closeness-centrality. research highlighted importance considering network long-term planning design mitigate UHI effect regions. A moderate grid a reasonable distribution green spaces throughout region is suggested reduce temperatures sustainably. indexes, such as are identified significant factors UHI. These findings have implications for planners, who can use this information create effects by reducing increasing straightness.

Language: Английский

Citations

14

Predicting Urban Land Use and Mitigating Land Surface Temperature: Exploring the Role of Urban Configuration with Convolutional Neural Networks DOI

Ghazaleh Tanoori,

Alì Soltani,

Atoosa Modiri

et al.

Journal of Urban Planning and Development, Journal Year: 2024, Volume and Issue: 150(3)

Published: June 24, 2024

Language: Английский

Citations

5

Monitoring dynamics of urban expansion using time series Landsat imageries and machine learning in Delhi NCR DOI
Mohd Waseem Naikoo, Ahmed Ali Bindajam,

Shahfahad

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: April 12, 2024

Language: Английский

Citations

4

Three novel cost-sensitive machine learning models for urban growth modelling DOI Creative Commons
Mohammad Ahmadlou, Mohammad Karimi,

Saad Sh. Sammen

et al.

Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)

Published: Jan. 1, 2024

This article addresses the class imbalance problem in urban gain modelling (UGM) of Tabriz and Isfahan megacities Iran by proposing novel cost-sensitive machine learning models, namely support vector (CSVM), random forest (CRF) artificial neural network (CANN). Random sampling, a frequently utilized method, fails to effectively tackle this issue biasing models towards no change samples, which outnumber samples. The results showed that CRF exhibited highest accuracy (AUC = 0.560), followed CANN 0.557) CSVM 0.448) Isfahan. In Tabriz, 0.809) 0.818) excelled, outperforming balanced sampling constructed with ANN, RF SVM AUROC ANN boosted 15% 2% validation. By emphasizing significance addressing appropriately, research highlights improvement outcomes achievable through especially case.

Language: Английский

Citations

4

Patterns and drivers of population in the borderlands of Mainland Southeast Asia DOI
Chiwei Xiao, Yuqian Liu, Yanzhao Yang

et al.

Habitat International, Journal Year: 2025, Volume and Issue: 157, P. 103321 - 103321

Published: Feb. 6, 2025

Language: Английский

Citations

0

Urban heat islands and energy consumption patterns: Evaluating renewable energy strategies for a sustainable future DOI
Muhammad Khalid Anser, Abdelmohsen A. Nassani,

Khalid M. Al-Aiban

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 3760 - 3772

Published: March 27, 2025

Language: Английский

Citations

0

On the road to urban sustainability: identifying major barriers to urban sustainability in Iran DOI
Hadi Alizadeh, Abolfazl Meshkini

Review of Regional Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Language: Английский

Citations

0