Smart City Gnosys
Smart city article details
| Title | Exposition Of Spatial Urban Growth Pattern Using Pso-Sleuth And Identifying Its Effects On Surface Temperature |
|---|---|
| ID_Doc | 25854 |
| Authors | Bharath H.A.; Nimish G.; Chandan M.C. |
| Year | 2020 |
| Published | Urban Ecology: Emerging Patterns and Social-Ecological Systems |
| DOI | http://dx.doi.org/10.1016/B978-0-12-820730-7.00004-5 |
| Abstract | Rapid population increase and migration have created a tremendous pressure on existing cities and their peripheries. Policy interventions such as sustainable development targeted for 2030 and Smart city mission both at global and local scale have paved way for visualizing and developing better scenarios that city administration can explore to create liveable cities. Geospatial technologies have been extensively used to develop solutions that can be implemented. Probabilistic modelling of future scenarios offers a robust alternative for resource utilization and further for maximizing sustainability through land-use pattern analysis. In this context, this chapter tries to analyse the land-use pattern of Bangalore, its urban growth pattern in last three decades and alteration in microclimate of the city in terms of Land Surface Temperature. Considering business as usual scenario, land-use modelling for the year 2025 is performed through an improvized modelling technique. This research communication tries to demonstrate a novel idea of integrating Particle Swarm Optimization (PSO) with SLEUTH for calibration of spatial-temporal footprint of urban growth from the year 1990 to 2017 for Bangalore. Results were evaluated and it was observed that PSO-SLEUTH performed substantially better compared to traditional Brute Force calibration method (BFM) with a significant improvement in computation time. Results indicate growth along the transport corridors with multiple agents fuelling the growth. Land-use pattern analysis explains how the city of Bangalore has converted to a concrete city and would become completely urbanized in a few years, with most of the vegetation lost making the city known as green city a grey city. © 2020 Elsevier Inc. |
| Author Keywords | Land surface temperature; Particle swarm optimization; Pattern recognition; SLEUTH; Urban modelling |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
60082  | 0.858 | Kumar V.; Agrawal S. | Urban Modelling And Forecasting Of Landuse Using Sleuth Model | International Journal of Environmental Science and Technology, 20, 6 (2023) |
| 48822 | 0.852 | Theres L.; Radhakrishnan S.; Rahman A. | Simulating Urban Growth Using The Cellular Automata Markov Chain Model In The Context Of Spatiotemporal Influences For Salem And Its Peripherals, India | Earth (Switzerland), 4, 2 (2023) |