Urban land surface temperature forecasting: a data-driven approach using regression and neural network models
The insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating u...
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| Format: | Article |
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2023.2299145 |
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| author | Nimish Gupta Bharath Haridas Aithal |
| author_facet | Nimish Gupta Bharath Haridas Aithal |
| author_sort | Nimish Gupta |
| collection | DOAJ |
| description | The insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating urban climate, particularly to explain the intensity of urban heat islands and to define the health and welfare of the planet as well as the living beings. Urbanization-driven landscape changes severely disrupt comfortable living in almost every city, necessitating monitoring and modelling historical, current, and likely future LSTs. This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. These models have been widely accepted for the efficient prediction of climatic parameters, including LST, over an urban area. The landscape, elevation, and LST trend served as input to the models for an accurate prediction of LST. The analysis was performed over the Kolkata Metropolitan Area (KMA) with an additional 10 km buffer to understand urban growth and its effect on the LST of the entire region. The two developed models (MLR and ANN) effectively anticipated the LST over the KMA region. A continual increment in the surface temperatures ranging from 1 °C to 4 °C, over existing and likely-predicted urban areas was comprehended. It was anticipated that the regions near the urban areas will also experience severe discomfort and heat waves without proper mitigation measures. This scientific literature provides essential insights for decision-makers, stakeholders, and government officials to articulate new policies and modify the existing ones to create a sustainable and livable urban environment for the inhabitants. |
| format | Article |
| id | doaj-art-82cba4e012304b7eaef22361e3952f41 |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-82cba4e012304b7eaef22361e3952f412025-08-20T01:59:21ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2023.2299145Urban land surface temperature forecasting: a data-driven approach using regression and neural network modelsNimish Gupta0Bharath Haridas Aithal1Ranbir and Chitra Gupta School of Infrastructure Design and Management, Indian Institute of Technology Kharagpur, Kharagpur, IndiaRanbir and Chitra Gupta School of Infrastructure Design and Management, Indian Institute of Technology Kharagpur, Kharagpur, IndiaThe insinuations of the ailments associated with the unrestrained and disorganized proliferation of artificial impervious materials over natural surfaces are prevalent among city dwellers. These impacts can be comprehended by estimating land surface temperature (LST), as it is vital for evaluating urban climate, particularly to explain the intensity of urban heat islands and to define the health and welfare of the planet as well as the living beings. Urbanization-driven landscape changes severely disrupt comfortable living in almost every city, necessitating monitoring and modelling historical, current, and likely future LSTs. This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. These models have been widely accepted for the efficient prediction of climatic parameters, including LST, over an urban area. The landscape, elevation, and LST trend served as input to the models for an accurate prediction of LST. The analysis was performed over the Kolkata Metropolitan Area (KMA) with an additional 10 km buffer to understand urban growth and its effect on the LST of the entire region. The two developed models (MLR and ANN) effectively anticipated the LST over the KMA region. A continual increment in the surface temperatures ranging from 1 °C to 4 °C, over existing and likely-predicted urban areas was comprehended. It was anticipated that the regions near the urban areas will also experience severe discomfort and heat waves without proper mitigation measures. This scientific literature provides essential insights for decision-makers, stakeholders, and government officials to articulate new policies and modify the existing ones to create a sustainable and livable urban environment for the inhabitants.https://www.tandfonline.com/doi/10.1080/10106049.2023.2299145Land surface temperatureland use/land coverCA-Markovmultiple linear regressionartificial neural network |
| spellingShingle | Nimish Gupta Bharath Haridas Aithal Urban land surface temperature forecasting: a data-driven approach using regression and neural network models Geocarto International Land surface temperature land use/land cover CA-Markov multiple linear regression artificial neural network |
| title | Urban land surface temperature forecasting: a data-driven approach using regression and neural network models |
| title_full | Urban land surface temperature forecasting: a data-driven approach using regression and neural network models |
| title_fullStr | Urban land surface temperature forecasting: a data-driven approach using regression and neural network models |
| title_full_unstemmed | Urban land surface temperature forecasting: a data-driven approach using regression and neural network models |
| title_short | Urban land surface temperature forecasting: a data-driven approach using regression and neural network models |
| title_sort | urban land surface temperature forecasting a data driven approach using regression and neural network models |
| topic | Land surface temperature land use/land cover CA-Markov multiple linear regression artificial neural network |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2023.2299145 |
| work_keys_str_mv | AT nimishgupta urbanlandsurfacetemperatureforecastingadatadrivenapproachusingregressionandneuralnetworkmodels AT bharathharidasaithal urbanlandsurfacetemperatureforecastingadatadrivenapproachusingregressionandneuralnetworkmodels |