Modeling urban land use dynamics using Markov-chain and cellular automata in Gondar City,Northwest Ethiopia

Modeling urban land-use dynamics is critical for urban experts’and infrastructure managers’planning.This study attempts to explore the land-use/land-cover(LULC)dynamics of Gondar using satellite images from 1984 to 2020.Markov-Chain and Cellular Automata(MC-CA)models have been recognized as performing well in predicting urban land-use change.However,only a few models work in Ethiopia in general,and no study in Gondar has applied this approach to study urban land-use patterns.Therefore,Gondar land-use/land cover changes of Gondar were predicted using the MC-CA model in IDRISI.The built-up area in Gondar city covered 1413 ha(3%of the total area)in 1984 and increased to 2380 ha(5%)in 1994;21153 ha(45.5%)in 2004;22622 ha(48.7%)in 2014;and 23427 ha(50.5%)in 2020.The area has been predicted to reach 57.5%in the 2050s,showing a faster increase that will cause a very vast loss of farmland.This will increase urban sprawl challenges as well as overall environmental disequilibrium in the preceding decade.Thus,innovative and careful structures and systems in urban planning are required to secure a sustainable urban future and to make our cities livable and competitive in the paradigm of sustainable cities.

Evaluation of a Micro-scale Wind Model's Performance over Realistic Building Clusters Using Wind Tunnel Experiments

The simulation performance over complex building clusters of a wind simulation model（Wind Information Field Fast Analysis model, WIFFA） in a micro-scale air pollutant dispersion model system（Urban Microscale Air Pollution dispersion Simulation model, UMAPS） is evaluated using various wind tunnel experimental data including the CEDVAL（Compilation of Experimental Data for Validation of Micro-Scale Dispersion Models） wind tunnel experiment data and the NJU-FZ experiment data（Nanjing University-Fang Zhuang neighborhood wind tunnel experiment data）. The results show that the wind model can reproduce the vortexes triggered by urban buildings well, and the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of the building shapes and the determination of the key zone sizes. The computational efficiencies of different cases are also discussed in this paper. The model has a high computational efficiency compared to traditional numerical models that solve the Navier–Stokes equations, and can produce very high-resolution（1–5 m） wind fields of a complex neighborhood scale urban building canopy（～ 1 km ×1km） in less than 3 min when run on a personal computer.

Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling(PLS-SEM)

The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling（SEM） demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM（PLS-SEM） and geographically-weighted regression（GWR） modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass（AGB）. The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems.

Modelling of Nature-Based Solutions (NBS) for Urban Water Management—Investment and Outscaling Implications at Basin and Regional Levels

This manuscript is an attempt to demonstrate effectiveness of nature-based solutions (NBS) and measures to reduce risk of flooding and environmental impact in urban settings. The nature-based solutions (NBS) were assessed as scenarios from experience of urban storm drainage and sewerage systems based on practices that improve urban water management through modelling using urban stormwater management model (SWMM). The model has been applied in a typical urban environment in the second city in Botswana, the City of Francistown, which has a population of more than one hundred thousand. By considering the 2-yr and 10-year storm events in a calibrated SWMM, NBS scenarios from a mix of low impact and drainage measures were considered. The considered NBS scenarios were used to determine their effectiveness in terms of reducing and controlling peak runoff, flood volumes, infiltration and evapotranspiration in the study area, which are vital in assessing the opportunity and challenge for sustainable management of water resources and associated tradeoff of investments in the urban contexts. The study demonstrates the usefulness of implementing effective measures for achieving NBS in urban context and possibility of outscaling at basin and regional levels.

Unified Data Model of Urban Air Pollution Dispersion and 3D Spatial City Model:Groundwork Assessment towards Sustainable Urban Development for Malaysia

Understanding the behavior of urban air pollution is important en route for sustainable urban development (SUD). Malaysia is on its mission to be a developed country by year 2020 comprehends dealing with air pollution is one of the indicators headed towards it. At present monitoring and managing air pollution in urban areas encompasses sophisticated air quality modeling and data acquisition. However, rapid developments in major cities cause difficulties in acquiring the city geometries. The existing method in acquiring city geometries data via ground or space measurement inspection such as field survey, photogrammetry, laser scanning, remote sensing or using architectural plans appears not to be practical because of its cost and efforts. Moreover, air monitoring stations deployed are intended for regional to global scale model whereby it is not accurate for urban areas with typical resolution of less than 2 km. Furthermore in urban areas, the pollutant dispersion movements are trapped between buildings initiating it to move vertically causing visualization complications which imply the limitations of existing visualization scheme that is based on two-dimensional (2D) framework. Therefore this paper aims is to perform groundwork assessment and discuss on the current scenario in Malaysia in the aspect of current policies towards SUD, air quality monitoring stations, scale model and detail discussion on air pollution dispersion model used called the Operational Street Pollution Model (OSPM). This research proposed the implementation of three-dimensional (3D) spatial city model as a new physical data input for OSPM. The five Level of Details (LOD) of 3D spatial city model shows the scale applicability for the dispersion model implementtation. Subsequently 3D spatial city model data commonly available on the web, by having a unified data model shows the advantages in easy data acquisition, 3D visualization of air pollution dispersion and improves visual analysis of air quality monitoring in urban areas.

Improving the WRF/urban modeling system in China by developing a national urban dataset

Accurate modeling of urban climate is essential to predict potential environmental risks in cities.Urban datasets,such as urban land use and urban canopy parameters(UCPs),are key input data for urban climate models and largely affect their performance.However,access to reliable urban datasets is a challenge,especially in fast urbanizing countries.In this study,we developed a high-resolution national urban dataset in China(NUDC)for the WRF/urban modeling system and evaluated its effect on urban climate modeling.Specifically,an optimization method based on building morphology was proposed to classify urban land use types.The key UCPs,including building height and width,street width,surface imperviousness,and anthropogenic heat flux,were calculated for both single-layer Urban Canopy Model(UCM)and multiple-layer Building Energy Parameterization(BEP).The results show that the derived morphological-based urban land use classification could better reflect the urban characteristics,compared to the socioeconomic-function-based classification.The UCPs varied largely in spatial within and across the cities.The integration of the developed urban land use and UCPs datasets significantly improved the representation of urban canopy characteristics,contributing to a more accurate modeling of near-surface air temperature,humidity,and wind in urban areas.The UCM performed better in the modeling of air temperature and humidity,while the BEP performed better in the modeling of wind speed.The newly developed NUDC can advance the study of urban climate and improve the prediction of potential urban environmental risks in China.

AN APPROACH ON SEVERAL PROBLEMS OF CHINA'S URBANIZATION DEVELOPMENT

China, having urique national conditions, is facing a basic contradiction between large numbers of less educated surplus rural labor forces and limited capacities of urban employment and infrastructure on the urbanization process.Hence, the urbanization model should prevail in comprehensive and several ways: (1)On occupational and spatial shift rural surplus labors, two fashions of 'leaving land bot not countryside' and 'leaving both land and countryside' should be integrated with joint effort of cities and countryside. (2) On urbanization motive, urban construction will be invested by not only the govermment (from the upper), but the locality, personnel and foreigners (from the lower). (3) On urban scales, metropolis,large, medium-sized and small cities and towns should play fully their roles to absorb rural surplus lanors. Thereby, the present urban development policy needs to be rectified, which this paper has discussed particularly. (4) On account of great regional disparities in China, urban development should perform different models in different regions. In addition, this paper has also focused on the problem and its primary countermeasures of 'labor force mass', a hot issue closely related to urbanization and rural labor transfer.

Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

Building Morphological Characteristics and Their Effect on the Wind in Beijing

An urban boundary layer model （UBLM） is improved by incorporating the effect of buildings with a sectional drag coefficient and a height-distributed canopy drag length scale. The improved UBLM is applied to simulate the wind fields over three typical urban blocks over the Beijing area with different height-towidth ratios. For comparisons, the wind fields over the same blocks are simulated by an urban sub-domain scale model resolving the buildings explicitly. The wind fields simulated from the two different methods are in good agreement. Then, two-dimensional building morphological characteristics and urban canopy parameters for Beijing are derived from detailed building height data. Finally, experiements are conducted to investigate the effect of buildings on the wind field in Beijing using the improved UBLM.

An iterative approach to an integrated land use and transportation planning tool for small urban areas

There has been increasing interests in developing land use models for small urban areas for various planning applications such as air quality conformity analysis. The output of a land use model can serve as a major input to a transportation model; conversely, transportation model output can provide a critical input to a land use model. The connection between the two models can be achieved by an accessibility measure. This paper presents an iterative approach to solving a regression-based land use model and a transportation model with combined trip distribution- assignment. A case study using data from a small urban area is presented to illustrate the application of the proposed modeling framework. Tests show that the procedures can converge, and the modeling framework can be a valuable tool for planners and decision-makers in evaluating land use policies and transportation investment strategies.

Ranking parameters in urban energy models for various building forms and climates using sensitivity analysis

Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization.Urban building energy modeling(UBEM)is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations,supporting the implementation of carbon emission reduction policies.Currently,most studies focus on the energy performance of archetype buildings under climate change,which is hard to obtain refined results for individual buildings when scaling up to an urban area.Therefore,this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas,by taking two urban neighborhoods comprising 483 buildings in Geneva,Switzerland as case studies.In this regard,GIS datasets and Swiss building norms were collected to develop an archetype library.The building heating energy consumption was calculated by the UBEM tool—AutoBPS,which was then calibrated against annual metered data.A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%.The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).The results showed a decrease of 22%–31%and 21%–29%for heating energy consumption,an increase of 113%–173%and 95%–144%for cooling energy consumption in the two neighborhoods by 2050.The average annual heating intensity dropped from 81 kWh/m^(2) in the current typical climate to 57 kWh/m^(2) in the SSP5-8.5,while the cooling intensity rose from 12 kWh/m^(2) to 32 kWh/m^(2).The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7%and 18.6%,respectively,in the SSP scenarios.The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

Impacts of Roof/Ground Mitigation Strategies on Improving the Urban Thermal Environment and Human Comfort over the Yangtze River Delta, China

The combined effects of global warming and the urban heat islands exacerbate the risk of urban heat stress. It is crucial to implement effective cooling measures in urban areas to improve the comfort of the thermal environment. In this study, the Weather Research and Forecasting Model(WRF), coupled with a single-layer Urban Canopy Model(UCM), was used to study the impact of heat mitigation strategies. In addition, a 5-km resolution land-cover dataset for China(ChinaLC), which is based on satellite remote sensing data, was adjusted and used, and 18 groups of numerical experiments were designed, to increase the albedo and vegetation fraction of roof/ground parameters. The experiments were conducted for four heatwave events that occurred in the summer of 2013 in the Yangtze River Delta urban agglomeration of China. The simulated results demonstrated that, for the single roof/ground schemes, the mitigation effects were directly proportional to the albedo and greening. Among all the experimental schemes, the superposed schemes presented better cooling effects. For the ground greening scheme, with similar net radiation flux and latent heat flux, its storage heat was lower than that of the roof greening scheme, resulting in more energy flux into the atmosphere, and its daytime cooling effect was not as good as that of the roof greening scheme. In terms of human thermal comfort(HTC), the improvement achieved by the ground greening scheme was better than any other single roof/ground schemes, because the increase in the relative humidity was small. The comprehensive evaluation of the mitigation effects of different schemes on the thermal environment presented in this paper provides a theoretical basis for improving the urban environment through rational urban planning and construction.

Archetype identification and urban building energy modeling for city-scale buildings based on GIS datasets

Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential.This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable.A case study was conducted for 68,966 buildings in Changsha city,China.First,clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets.Then,the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years.The year built of residential buildings was collected from the housing website.Moreover,twenty-two building types and three vintages were selected as archetype buildings to represent 59,332 buildings,covering 87.4%of the total floor area.Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings.Finally,monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus.The total electricity and natural gas use for the 59,332 buildings was 13,864 GWh and 23.6×10^(6) GJ.Three energy conservation measures were evaluated to demonstrate urban energy saving potential.The proposed methods can be easily applied to other cities in China.

Study on Effects of Building Morphology on Urban Boundary Layer Using an Urban Canopy Model

An urban canopy model is incorporated into the Nanjing University Regional Boundary Layer Model. Temperature simulated by the urban canopy model is in better agreement with the observation, especially in the night time, than that simulated by the traditional slab model. The coupled model is used to study the effects of building morphology on urban boundary layer and meteorological environment by changing urban area, building height, and building density. It is found that when the urban area is expanded, the urban boundary layer heat flux, thermal turbu- lence, and the turbulent momentum flux and kinetic energy all increase or enhance, causing the surface air temperature to rise up. The stability of urban atmospheric stratification is affected to different extent at different times of the day. When the building height goes up, the aerodynamic roughness height, zero plane displacement height of urban area, and ratio of building height to street width all increase. Therefore, the increase in building height results in the decrease of the surface heat flux, urban surface temperature, mean wind speed, and turbulent kinetic energy in daytime. While at night, as more heat storage is released by higher buildings, thermal turbulence is more active and surface heat flux increases, leading to a higher urban temperature. As the building density increases, the aerodynamic roughness height of urban area decreases, and the effect of urban canopy on radiation strengthens. The increase of building density results in the decrease in urban surface heat flux, momentum flux, and air temperature, the increase in mean wind speed, and the weakening of turbulence in the daytime. While at night, the urban temperature increases due to the release of more heat storage.

WRF/CHEM modeling of impacts of weather conditions modified by urban expansion on secondary organic aerosol formation over Pearl River Delta

In this paper, the online Weather Research and Forecasting and Chemistry （WRF/CHEM） model, coupled with urban canopy （UCM） and biogenic-emission models, is used to explore impacts of urban expansion on secondary organic aerosols （SOA） formation. Two scenarios of urban maps are used in WRF/CHEM to represent early 1990s （pre-urbanization） and current urban distribution in the Pearl River Delta （PRD）. Month-long simulation results using the above land-use scenarios for March 2001 show： （1） urbanization can increase monthly averaged temperatures by about 0.63 ℃, decrease monthly averaged 10-m wind speeds by 38%, increase monthly averaged boundary-layer depths by 80 m, and decrease monthly aver- aged water mixing ratio by 0.2g/kg. （2） Changes in meteorological conditions can result in detectable concentration changes of NOx, VOC, O3 and NO3 radicals. Urbanization decreases surface NOx and VOC concentrations by a maximum of 4 ppbv and 1.5 ppbv, respectively. Surface O3 and NO3 radical concentrations over major cities increase by about 2-4 ppbv and 4-12 pptv, respectively; areas with increasing O3 and NO3 radical concentrations generally coincide with the areas of temperature increase and wind speed reduction where NOx and VOC decrease. （3） Urbanization can induce 9% increase of SOA in Foshan, Zhongshan and west Guangzhou and 3% decrease in Shenzhen and Dongguan. Over PRD major cities, SOA from Aitken mode reduces by 30% but with more than 70% SOA from accumulate mode. Urbanization has stronger influence on SOA formation from Aitken mode. （4） Over the PRD, 55-65% SOA comes from aromatics precursors. Urbanization has strongest influence on aromatics precursors to produce SOA （14% increase）, while there is less influence on alkane precursors. Alkene precursors have negative contribution to SOA formation under urbanization situation.

Development of an Accurate Urban Modeling System Using CAD/GIS Data for Atmosphere Environmental Simulation

This paper presents an urban modeling system using CAD/GIS data for atmosphere environmental simulation, such as wind flow and contaminant spread in urban area. The CAD data is used for the shape modeling for the high-storied buildings and civil structures with complicated shape since the data for that is not included in the 3D-GIS data accurately. The unstructured mesh based on the tetrahedron element is employed in order to express the urban structures with complicated shape accurately. It is difficult to understand the quality of shape model and mesh by the conventional visualization technique. In this paper, the stereoscopic visualization using virtual reality (VR) technology is employed for the verification of the quality of shape model and mesh. The present system is applied to the atmosphere environmental simulation in urban area and is shown to be an useful planning and design tool to investigate the atmosphere environmental problem.

A review of computer graphics approaches to urban modeling from a machine learning perspective

Urban modeling facilitates the generation of virtual environments for various scenarios about cities.It requires expertise and consideration,and therefore consumes massive time and computation resources.Nevertheless,related tasks sometimes result in dissatisfaction or even failure.These challenges have received significant attention from researchers in the area of computer graphics.Meanwhile,the burgeoning development of artificial intelligence motivates people to exploit machine learning,and hence improves the conventional solutions.In this paper,we present a review of approaches to urban modeling in computer graphics using machine learning in the literature published between 2010 and 2019.This serves as an overview of the current state of research on urban modeling from a machine learning perspective.

Urban Stormwater Modeling with Local Inertial Approximation Form of Shallow Water Equations: A Comparative Study

This study focused on the performance and limitations of the local inertial approximation form model(LIM)of the shallow water equations(SWEs)when applied in urban flood modeling.A numerical scheme of the LIM equations was created using finite volume method with a first-order spatiotemporal Roe Riemann solver.A simplified urban stormwater model(SUSM)considering surface and underground dual drainage system was constructed based on LIM and the US Environmental Protection Agency Storm Water Management Model.Moreover,a complete urban stormwater model(USM)based on the SWEs with the same solution algorithm was used as the evaluation benchmark.Numerical results of the SUSM and USM in a highly urbanized area under four rainfall return periods were analyzed and compared.The results reveal that the performance of the SUSM is highly consistent with that of the USM but with an improvement in computational efficiency of approximately 140%.In terms of the accuracy of the model,the SUSM slightly underestimates the water depth and velocity and is less accurate when dealing with supercritical flow in urban stormwater flood modeling.Overall,the SUSM can produce comparable results to USM with higher computational efficiency,which provides a simplified and alternative method for urban flood modeling.

Web-based visualization of large 3D urban building models

Adaptive rendering of large urban building models has become an important research issue in three-dimensional(3D)geographic information system(GIS)applications.This study explores a way for rendering web-based 3D urban building models.A client-server hybrid rendering approach is presented for large 3D city models,stored on a remote server through a network.The approach combines an efficient multi-hierarchical building representation with a novel image-based method,3D image impostor generated on demand by a remote server.This approach allows transferring complex scenes progressively while keeping high visualization quality.We also evaluated the rendering and data transferring performance of the proposed approach.