Analyzing long term urban growth trends can provide valuable insights into a city’s future growth.This study employs LANDSAT satellite images from 1990,2000,2010 and 2019 to perform a spatiotemporal assessment and pr...Analyzing long term urban growth trends can provide valuable insights into a city’s future growth.This study employs LANDSAT satellite images from 1990,2000,2010 and 2019 to perform a spatiotemporal assessment and predict Ahmedabad’s urban growth.Land Use Land Change(LULC)maps developed using the Maximum Likelihood classifier produce four principal classes:Built-up,Vegetation,Water body,and“Others”.In between 1990-2019,the total built-up area expanded by 130%,132 km^(2) in 1990 to 305 km^(2) in 2019.Rapid population growth is the chief contributor towards urban growth as the city added 3.9 km^(2) of additional built-up area to accommodate every 100,000 new residents.Further,a Multi-Layer Perceptron-Markov Chain model(MLP-MC)predicts Ahmedabad’s urban expansion by 2030.Compared to 2019,the MLP-MC model predicts a 25%and 19%increase in Ahmedabad’s total urban area and population by 2030.Unaltered,these trends shall generate many socio-economic and environmental problems.Thus,future urban development policies must balance further development and environmental damage.展开更多
A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors.This paper highlights limitations of this approach by contrasting it with a probabilis...A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors.This paper highlights limitations of this approach by contrasting it with a probabilistic uncertainty/sensitivity simulation approach.Latin hypercube sampling(LHS)generates 15000 unique model configurations to assess the effects of weather,physical and operational uncertainties on the annual and peak cooling energy demands for a residential building which situated in a hot and dry climatic region.Probabilistic simulations predicted 0.22–2.17 and 0.45–1.62 times variation in annual and peak cooling energy demands,respectively,compared to deterministic simulation.A novel density-based global sensitivity analysis(SA),i.e.,PAWN,is adopted to identify dominant input uncertainties.Unlike traditional SA methods,PAWN allows simultaneous treatment of continuous and categorical inputs from a generic input-output sample.PAWN is favourable when computational resources are limited and model outputs are skewed or multi-modal.For annual and peak cooling demands,the effects of weather and operational parameters associated with airconditioner and window operation are much stronger than these of other parameters considered.Consequently,these parameters warrant greater attention during modelling and simulation stages.Bootstrapping and convergence analysis also confirm the validity of these results.展开更多
基金Zero Peak Energy Demand for India(ZED-I)and Engineering and Physics Research Council EPSRC,No.EP/R008612/1。
文摘Analyzing long term urban growth trends can provide valuable insights into a city’s future growth.This study employs LANDSAT satellite images from 1990,2000,2010 and 2019 to perform a spatiotemporal assessment and predict Ahmedabad’s urban growth.Land Use Land Change(LULC)maps developed using the Maximum Likelihood classifier produce four principal classes:Built-up,Vegetation,Water body,and“Others”.In between 1990-2019,the total built-up area expanded by 130%,132 km^(2) in 1990 to 305 km^(2) in 2019.Rapid population growth is the chief contributor towards urban growth as the city added 3.9 km^(2) of additional built-up area to accommodate every 100,000 new residents.Further,a Multi-Layer Perceptron-Markov Chain model(MLP-MC)predicts Ahmedabad’s urban expansion by 2030.Compared to 2019,the MLP-MC model predicts a 25%and 19%increase in Ahmedabad’s total urban area and population by 2030.Unaltered,these trends shall generate many socio-economic and environmental problems.Thus,future urban development policies must balance further development and environmental damage.
基金The authors would like to acknowledge the funding received from the Department of Science and Technology,Government of India(DST/TMD/UKBEE/2017/17)Projects:Zero Peak Energy Demand for India(ZED-I)and Engineering and Physics Research Council EPSRC(EP/R008612/1).
文摘A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors.This paper highlights limitations of this approach by contrasting it with a probabilistic uncertainty/sensitivity simulation approach.Latin hypercube sampling(LHS)generates 15000 unique model configurations to assess the effects of weather,physical and operational uncertainties on the annual and peak cooling energy demands for a residential building which situated in a hot and dry climatic region.Probabilistic simulations predicted 0.22–2.17 and 0.45–1.62 times variation in annual and peak cooling energy demands,respectively,compared to deterministic simulation.A novel density-based global sensitivity analysis(SA),i.e.,PAWN,is adopted to identify dominant input uncertainties.Unlike traditional SA methods,PAWN allows simultaneous treatment of continuous and categorical inputs from a generic input-output sample.PAWN is favourable when computational resources are limited and model outputs are skewed or multi-modal.For annual and peak cooling demands,the effects of weather and operational parameters associated with airconditioner and window operation are much stronger than these of other parameters considered.Consequently,these parameters warrant greater attention during modelling and simulation stages.Bootstrapping and convergence analysis also confirm the validity of these results.
