Amir Siddique

Tianjin University, School of Architecture, Tianjin University, PhD student

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A PhD researcher with experience in urban planning and spatial analysis. As an urban analyst, I am concerned with the design and management of the built environment to achieve social, economic and environmental goals, and with the analysis and interpretation of spatial data as a basis for urban planning and decision-making. I have a deep understanding of the social, economic and environmental factors that inﬂuence urban development and the tools and techniques used to analyse and manage spatial data. Through my training and experience, I have a broad range of skills including spatial data analysis, community engagement, land use planning, environmental planning, and geographic information systems (GIS). My experience includes working with various stakeholders to design and implement urban plans and policies that promote sustainable development and equitable outcomes.

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Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan

Urban Climate

Urban pluvial flooding (UPF) has emerged as a serious natural hazard, especially in recent years.... more Urban pluvial flooding (UPF) has emerged as a serious natural hazard, especially in recent years. Previous research on UPF prediction has mainly focused on hydrological models, which required a large amount of data. However, a data-driven method can significantly reduce the computa-tional cost by using rainfall amounts to predict pluvial flooding. Intensity-duration-frequency (IDF) curves using the Gumbel method can provide a better interpretation of the correlation between rainfall intensity, duration, and probability of occurrence of a given rainfall amount. In this study, machine learning models (ML) for rainfall amounts were used to identify flood points in a case study conducted in Karachi, Pakistan. Thirteen inundation factors were used for the ML models, including a new factor, curve number. Ten ML models were applied first on training and then on validation data, yielding the inundation points. The training and validation process of the model included 384 flood points. Several statistics were used to verify the performance and accuracy of the model. We found that the Light Gradient Boost Machine and Random Forest Classifier models were the most accurate in training and validating the model, while the Decision Tree and K-Nearest Neighbor models were the least accurate in training and validating the model. The study provides valuable information for decision-makers to protect communities from flood hazards by incorporating the likely intensity and duration of rainfall events and carefully selecting influencing factors into flood event prediction models.

Modeling the Impact and Risk Assessment of Urbanization on Urban Heat Island and Thermal Comfort Level of Beijing City, China (2005–2020)

Sustainability

Rapid urbanization poses a threat to various ecosystem services. Beijing has undergone extensive ... more Rapid urbanization poses a threat to various ecosystem services. Beijing has undergone extensive infrastructure development in recent years. The study aims to extract land surface temperature (LST) and land use cover (LUC) data from satellite imagery, identify urban heat island (UHI) areas in Beijing, and determine the correlation between LST, LUC, NDVI, and BUI. It will also investigate the relationship between UHI and built/unbuilt areas, evaluate thermal comfort in Beijing using UTFVI, and assess the ecological quality of different land use types using the Ecological Evaluation Index (EEI). The results can inform urban planning and management in rapidly urbanizing and climate-changing regions. Changes in LUC and other activities affect the distribution of LST. For the study years (2005–2020), the estimated mean LST in Beijing was 24.72 °C, 27.07 °C, 26.22 °C, and 27.03 °C, respectively. A significant positive correlation (r = 0.96 p > 0.005) was found between LST and urban are...

Spatiotemporal Impact of Urbanization on Urban Heat Island and Urban Thermal Field Variance Index of Tianjin City, China

Buildings, 2022

The rapid infrastructure development in densely populated areas has had several negative impacts.... more The rapid infrastructure development in densely populated areas has had several negative impacts. Increases in urbanization have led to increased LST, and urban ecological systems have been negatively affected. Urban heat islands (UHIs) can be mitigated by understanding how current and future LST phenomena are linked to changes in landscape composition and land use cover (LUC). This study investigated the multi-scale spatial analysis of LUC and LST in Tianjin using remote sensing and GIS data. We used Landsat data from 2005 to 2020 to examine the effects of LUC on LST in urban agglomeration. According to the Urban Thermal Field Variance Index (UTFVI), the city’s ecological evaluation was carried out. Results show that changes in LUC and other anthropogenic activities affect the spatial distribution of LST. For the study years (2004–2009), the estimated mean LST in Tianjin was 25.32 °C, 26.73 °C, 27.62 °C, and 27.93 °C. Between LST and urban areas with other infrastructures, and NDBI...

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