Land surface temperature estimating in urbanized landscapes using artificial neural networks

Noise pollution prediction and seasonal comparison in urban parks using a coupled GIS- artificial neural network model

Noise pollution is a challenging environmental issue in densely built urban areas and requires a holistic understanding of its sources and alleviation processes. Taking Isfahan City in Iran as a typical case, this study developed a combined GIS-artificial neural network (ANN) model to predict the spatio-temporal contribution of low-width parks to poise pollution mitigation. The 30-min equivalent sound level was measured at 100 stations in six urban parks (with a total area of 55.84 ha) under stable and controlled winter and summer conditions. The noise level predicting variables were hypothesized to be the area of vegetation cover; NDVI-based vegetation density and standard deviation (std); vegetation height; and road coverage measured within 100-, 200-, and 300-m radius buffer rings drown around each noise sampling station. These predictors were introduced to a multi-layer perceptron ANN model to identify and compare the most important noise alleviation variables among the selected predictors. The mean noise levels ranged from 67.23 to 70.57 dB. The number of vehicles showed an insignificant temporal difference, indicating that the noise source was relatively constant between the seasons. The ANN model performed satisfactorily in both seasons with SSE values of < 0.03. The Mann-Whitney U test showed a significant difference in the predicted noise levels between summer and winter. This study highlighted the efficiency of the combined GIS-ANN model in predicting distant-dependent urban processes, especially noise pollution whose levels and variability are essential in formulating urban land-use management.

Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea

Understanding the relationship between land use/land cover (LULC) and land surface temperature (LST) has long been an area of interest in urban and environmental study fields. To examine this, existing studies have utilized both white-box and black-box approaches, including regression, decision tree, and artificial intelligence models. To overcome the limitations of previous models, this study adopted the explainable artificial intelligence (XAI) approach in examining the relationships between LULC and LST. By integrating the XGBoost and SHAP model, we developed the LST prediction model in Seoul and estimated the LST reduction effects after specific LULC changes. Results showed that the prediction accuracy of LST was maximized when landscape, topographic, and LULC features within a 150 m buffer radius were adopted as independent variables. Specifically, the existence of surrounding built-up and vegetation areas were found to be the most influencing factors in explaining LST. In this study, after the LULC changes from expressway to green areas, approximately 1.5 °C of decreasing LST was predicted. The findings of our study can be utilized for assessing and monitoring the thermal environmental impact of urban planning and projects. Also, this study can contribute to determining the priorities of different policy measures for improving the thermal environment.

Exploring the relationship between LST and land cover of Bengaluru by concentric ring approach

The present study aims at investigating the impact of land cover features in enhancing or mitigating Land Surface Temperature (LST) in a semi-arid tropical metropolitan city of Bengaluru, India. Spatial distribution of LST and land cover types of the area were examined in the circumferential direction, and the contribution of land cover classes on LST was studied over 28 years. Urban growth and LST were modelled using Landsat and MODIS data for the years 1989, 2001, 2005 and 2017 based on the concentric ring approach. The study provides an efficient methodology for modelling and parameterisation of LST and urban growth by fitting an inverse S-curve into urban density (UD) and mean LST data. In addition, multiple linear regression models which could effectively predict the LST distribution based on land cover types were developed for both day and night time. Based on the analysis of remotely sensed data for LST, it is observed that over the years, urban core area has increased circumferentially from 5 to 10 km, and the urban growth has spread towards outskirts beyond 15 km from the city centre. As urban expansion occurs, the area under the study experiences an expansive cooling effect during day time; at night, an expansive heating effect is experienced in accordance with the growth in UD in the suburban area and outskirts. The regression models that were developed have relatively high accuracy with R² value of more than 0.94 and could explain the relationship between LST and land cover types. The study also revealed that there exists a negative correlation between urban, vegetation, water body and LST during day time while a positive correlation is observed during night. Thus, this study could assist urban planners and policymakers in understanding the scientific basis for urban heating effect and predict LST for the future development for implementing green infrastructure. The proposed methodology could be applied to other urban areas for quantifying the distribution of LST and different land cover types and their interrelationships.

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review

Spatio-temporal land-use change modeling, simulation, and prediction have become one of the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy, the ability for improvement, and the capability for integration of available models. Therefore, many types of models such as dynamic, statistical, and machine learning (ML) models have been used in the geographic information system (GIS) environment to fulfill the high-performance requirements of land-use modeling. This paper provides a literature review on models for modeling, simulating, and predicting land-use change to determine the best approach that can realistically simulate land-use changes. Therefore, the general characteristics of conventional and ML models for land-use change are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various dynamic, statistical, and ML models are determined according to the analysis and discussion of the characteristics of these models. The results of the review confirm that ML models are the most powerful models for simulating land-use change because they can include all driving forces of land-use change in the simulation process and simulate linear and non-linear phenomena, which dynamic models and statistical models are unable to do. However, ML models also have limitations. For instance, some ML models are complex, the simulation rules cannot be changed, and it is difficult to understand how ML models work in a system. However, this can be solved via the use of programming languages such as Python, which in turn improve the simulation capabilities of the ML models.