Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process

Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City and its environs (DCEN) to study the future scenario in the years 2025, 2030, and 2040 based on the 2018 simulation results from 2007 and 2018 LULC maps. This study evaluates the spatial and temporal variations of future LULCC, including the future potential landscape risk (FPLR) area of the 2008 great (8.0 Mw) earthquake of south-west China. The Cellular automata–Markov chain (CA-Markov) model and multicriteria based analytical hierarchy process (MC-AHP) approach have been considered using the integration of remote sensing and GIS techniques. The analysis shows future LULC scenario in the years 2025, 2030, and 2040 along with the FPLR pattern. Based on the results of the future LULCC and FPLR scenarios, we have provided suggestions for the development in the close proximity of the fault lines for the future strong magnitude earthquakes. Our results suggest a better and safe planning approach in the Belt and Road Corridor (BRC) of China to control future Silk-Road Disaster, which will also be useful to urban planners for urban development in a safe and sustainable manner

Evaluation and Prediction of Land-Use Changes using the CA_Markov Model

AbstractThe purpose of this study was to model and predict temporal and spatial patterns of land-use change in the Zayandehrud basin. In this research, the CA-Markov prediction model was used to simulate and predict land-use change. First, the land-use changes from 1996 to 2018 were studied and then the future changes for 2030 and 2050 were simulated. Afterward, the future land-use scenarios were designed. The model was validated by comparing the simulated map of 2018 with the real map, and the kappa coefficient of 94 % was utilized to evaluate the model. Based on the results, the Built-up land-use was altered from 13016 hectares in 1996 to 154194 hectares in 2050. This outcome necessitates the management of the future development of the city. Furthermore, the amount of agricultural land was varied from 177067 hectares in 1996 to 40,000 hectares in 2050. Among all the changes, agricultural lands attracted the most attention and concerns. The results indicated the land-use changes in the form of urban areas and reducing area of agricultural lands. Such alterations were taken place in two distinct stages: urban lands have been developing since 2013, with a direct impact on the reduction of vegetation due to the conversion of agricultural lands into other land-uses. The dynamic trend of changes has also been confirmed and intensified since 1996. In 2018, a significant area of agricultural lands was converted into urban and industrial areas. In addition, the agricultural and orchard lands were 74057 hectares in 2018 and can be reduced to 40,000 hectares by 2050. It revealed 34057 hectares lost as compared to the agricultural and orchard lands in 2018. The present study depicts that the expansion of urban and industrial activities and reducing the level of agricultural land in the region requires more attention and care in land management. Extended Abstract:Introduction: Land use/Land cover (LULC) change is one of the main issues of sustainable development. To provide a rational science for regional planning decisions and sustainable development, land use pattern prediction models based on past preliminary information can be used to construct future scenarios of land-use changes. Modeling and predicting land use changes provide an interesting perspective for applications in planning units such as river basins and make it an effective tool for analyzing the causal dynamics of the future landscape under different scenarios. Land-use models are considered a powerful tool for understanding the spatio-temporal pattern of land-use changes, such as the Markov chain, cellular automation, and hybrid models based on these methods, which are widely used to simulate the spatial and temporal dimensions of land use. In the present study, land use changes prediction was performed using a combined model of cellular automation and the Markov chain (CA-Markov) to simulate temporal and spatial land-use patterns. The present study tries to predict land-use changes in the Zayandehrood river basin. The Zayandehrud basin is currently facing major environmental problems (such as water resources scarcity, population growth, urban development, and agricultural land degradation). Therefore, it is essential to evaluate land-use changes for this sensitive basin. In particular, the objectives of the research include two stages: 1) patial modeling of land-use change, and 2) predicting spatio-temporal patterns of land-use changes in the Zayandehrud river basin.Therefore, in the present research, land-use changes from 1996 to 2018 were investigated and future changes for 2030 and 2050 were simulated. Methodology: In this research, land-use changes modeling was performed in three time periods 1996 to 2013 (17-year period), 2013 to 2018 (5-year period), and 2018 to 2030 and 2050. The purpose of modeling is to determine the capabilities of the Markov chain model and integrate it with cellular automation to detect land-use changes. The images were classified into 4 classes: agriculture and gardens, built-up (urban areas, airport, and road), industrial towns, and other land uses (abandoned lands and fallow, rangeland, water areas). Finally, land-use changes modeling was performed in the period 1996 to 2050 (54-year period). The steps of the research method are as follows:Step 1: Pre-processing of satellite images: Radiometric correction was applied to the images. Next, the images were processed using the FLAASH module in ENVI5.3 software to reduce atmospheric interference. Then, by synthesizing the name and wavelength of the bands, image storage, mosaic, and mask clipping, a preprocessed remote sensing image was generated. Finally, the preprocessed remote sensing image was obtained.Step 2: Processing satellite images: Types of land use images in the area in ENVI 5.3 were extracted using visual interpretation and supervised classification methods. Land use classification algorithms were used to estimate the three main land-use classes (agriculture, urban, and industrial development). The principal component analysis method was performed on the images and was identified agricultural by high resolution. Land use classification for 1996, 2013, and 2018 was done with a classification approach based on the decision tree. To classify the images, maximum likelihood methods, artificial neural networks, and support vector machines were used. The final classification was performed using decision tree analysis. Finally, prediction of land-use changes was performed on images by performing the CA_Markov analysis in TerrSet software.Step 3: Post-processing of satellite images: Using Google Earth and cross-tab analysis, TerrSet software evaluated the accuracy of classifying land-use thematic maps. Using the existing database, a validation process was performed to ensure the accuracy of the model in predicting land-use changes for the forecasted 2018 map. The accuracy of the simulated model of land-use change in 2018 was validated and then compared with the actual map of the same year. The validation process was performed by generating the kappa coefficient. Discussion: In this study, land-use changes in the Zayandehrood basin were identified and investigated. The results showed that land-use changes are in the form of urban development and reduction of agricultural land use. Such changes have occurred in two distinct stages. First, urban land expansion has prevailed since 2013, with a direct impact on declining vegetation as a result of the conversion of agricultural land to other land uses. The dynamic trend of changes has also been confirmed and intensified since 1996. Because in 2018, a significant area of agricultural lands was converted into urban and industrial areas. Future scenarios based on the CA-Markov model provide valuable information about future land use and land cover changes in the study area. This study can identify land-use changes in different periods and depict the increase or decrease of important land uses in the region. According to the study of Motlagh et al. (2020), land-use changes were studied based on three possible scenarios (i.e. the current trend of land use growth, conservation of agricultural lands, and urban development forecast). Future scenarios for 2030 and 2050 estimate that there will be a significant reduction in vegetation and agricultural lands and orchards and continued urban and industrial development in areas along the Zayandehrood basin. Expansion of the agricultural sector along with the conservation of natural resources is not only one of the most important challenges of sustainable development in the Zayandehrud basin but is also essential for future strategic land use plans. Compilation of instructions for sustainable agricultural development can be a way to strike a balance between nature conservation and economic development in the region. Conclusion: In summary, this study demonstrates how the proposed CA-Markov model is used to better simulate land use complex and dynamic changes over time. Of all the land-use changes, the most worrying is the situation in the region for agricultural lands. If the current trend of land use continues, we estimate that by 2050, its area will be halved, and such changes in the landscape will undoubtedly change the entire ecosystem of the basin, emphasizing that the negative effects on the vegetation of the basin have a direct impact on the economic sector of the region because maintaining the quality of the environment of the Zayandehrood river basin is essential for ecotourism. Therefore, the management and planning of the basin are highly recommended to preserve its unique ecosystem, as well as to protect the vegetation in the area. The methods and results of this study will be useful for policymakers and urban planners for precise planning of the region to be able to manage the city using farms and conserving water resources and urban infrastructure development planning for environmentally sustainable development. Keywords: Land-Use Changes, Cellular Automation, the Markov Chain, Zayandehrud River Basin. References- Asgarian, A., Soffianian, A., Pourmanafi, S., & Bagheri, M. (2018). Evaluating the spatial effectiveness of alternative urban growth scenarios inprotecting cropland resources: A case of mixed agricultural-urbanized landscape in central Iran. Journal of Sustainable Cities and Society, 43, 197-207.- Assaf, C., Adamsa, C., Ferreira, F. F., & Françac, H. (2021). Land use and cover modeling as a tool for analyzing nature conservation policies – A case study of Juréia-Itatins. Journal of Land Use Policy, 100, 104895.- Aung, T. S., Fischer, T. B., & Buchanan, J. (2020). Land use and land cover changes along the China-Myanmar oil and gas pipelines-Monitoring infrastructure development in remote conflict-prone regions. PloS one, 15(8), e0237806.- Baqa, M. F., Chen, F., Lu, L., Qureshi, S., Tariq, A., Wang, S., … & Li, Q. (2021). Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan. Land, 10(7), 700.- Cunha, E. R. D., Santos, C. A. G., da Silva, R. M., Bacani, V. M., & Pott, A. (2021). Future scenarios based on a CA-Markov land use and land cover simulation model for a tropical humid basin in the Cerrado/Atlantic forest ecotone of Brazil. Journal of Land Use Policy, 101, 105141.- Dey, N. N., Al Rakib, A., Kafy, A. A., & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using Multi-layer perception Markov chain model in Rajshahi City, Bangladesh. Journal of Environmental Challenges, 4, 100148.- Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9), e05092.- Ghosh, S., Chatterjee, N. D., & Dinda, S. (2021). Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India. Journal of Sustainable Cities and Society, 68, 102773.- Huang, Y., Yang, B., Wang, M., Liu, B., & Yang, X. (2020). Analysis of the future land cover change in Beijing using CA–Markov chain model. Journal of Environmental Earth Sciences, 79(2), 1-12.- Ji, G., Lai, Z., Xia, H., Liu, H., & Wang, Z. (2021). Future runoff variation and flood disaster prediction of the yellow river basin based on CA-Markov and SWAT. Land, 10(4), 421.- Khwarahm, N. R., Qader, S., Ararat, K., & Al-Quraishi, A. M. F. (2021). Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model. Journal of Earth Science Informatics, 14(1), 393–406.- Li, Q., Wang, L., Gul, H. N., & Li, D. (2021). Simulation and optimization of land use pattern to embed ecological suitability in an oasis region: A case study of Ganzhou district, Gansu province, China. Journal of Environmental Management, 287, 112321.- Matlhodi, B., Kenabatho, P. K., Parida, B. P., & Maphanyane, J. G. (2021). Analysis of the future land use land cover changes in the gaborone dam catchment using ca-markov model: Implications on water resources. Journal of Remote Sensing, 13(13), 2427.- Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Journal of Landscape and Urban Planning, 99(2), 141–153.- Motlagh, Z. K., Lotfi, A., Pourmanafi, S., Ahmadizadeh, S., & Soffianian, A. (2020). Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: Integration of remote sensing, CA Markov, and landscape metrics. Journal of Environmental Monitoring and Assessment, 192(11), 1-19.- Munthali, M. G., Mustak, S., Adeola, A., Botai, J., Singh, S. K., & Davis, N. (2020). Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Remote Sensing Applications: Society and Environment, 17, 100276.- Nath, B., Wang, Z., Ge, Y., Islam, K., Singh, R. P., & Niu, Z. (2020). Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process. International Journal of Geo-Information, 9(2), 134.- Rahnama, M. R. (2021). Forecasting land-use changes in Mashhad metropolitan area using cellular Automata and Markov chain model for 2016-2030. Journal of Sustainable Cities and Society, 64, 102548.- Ruben, G. B., Zhang, K., Dong, Z., & Xia, J. (2020). Analysis and projection of land-use/land-cover dynamics through scenario-based simulations using the CA-Markov model: A case study in guanting reservoir basin, China. Sustainability, 12(9), 3747.- Sibanda, S., & Ahmed, F. (2021). Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub‑catchment, Zimbabwe. Journal of Modeling Earth Systems and Environment, 7(1), 57–70.- Silver, D., & Silva, T. H. (2021). A Markov model of urban evolution: Neighbourhood change as a complex process. Plos One, 16(1), e0245357.- Tang, F., Fu, M., Wang, L., Song, W., Yu, J., & Wu, Y. (2021). Dynamic evolution and scenario simulation of habitat quality under the impact of land-use change in the Huaihe river economic belt, China. Plos One, 16(4), e0249566.- Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan Aqil Tariq and Hong Shu. Remote Sensing, 12(20), 3402.- Tavangar, Sh., Moradi, H., Massah Bavani, A., & Gholamalifard, M. (2019). A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood watershed, Iran. Geocarto International, 36(10), 1100-1116.- Vinayak, B., Lee, H. S., & Gedem, S. (2021). Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based markov chain model. Sustainability, 13(2), 471.- Wang, Q., Guan, Q., Lin, J., Luo, H., Tan, Z., & Ma, Y. (2021). Simulating land use/land cover change in an arid region with the coupling models. Journal of Ecological Indicators, 122, 107231.- Wang, H., & Hu, Y. (2021). Simulation of biocapacity and spatial-temporal evolution analysis of Loess Plateau in northern shaanxi based on the CA–Markov model. Sustainability, 13(11), 5901.- Wang, S. W., Munkhnasan, L., & Lee, W. (2021). Land use and land cover change detection and prediction in Bhutan’s high-altitude city of Thimphu, using cellular automata and Markov chain. Journal of Environmental Challenges, 2, 100017.- Zhou, L., Dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Journal of Sustainable Cities and Society, 55, 102045

Linking Climate Change and Socio-economic Impact for Long-term Urban Growth in Three Mega-cities

Urbanization has become a global trend under the impact of population growth, socio-economic development, and globalization. However, the interactions between climate change and urban growth in the context of economic geography are unclear due to missing links in between the recent planning megacities. This study aims to conduct a multi-temporal change analysis of land use and land cover in New York City, City of London, and Beijing using a cellular automata-based Markov chain model collaborating with fuzzy set theory and multi-criteria evaluation to predict the city\u27s future land use changes for 2030 and 2050 under the background of climate change. To determine future natural forcing impacts on land use in these megacities, the study highlighted the need for integrating spatiotemporal modeling analyses, such as Statistical Downscale Modeling (SDSM) driven by climate change, and geospatial intelligence techniques, such as remote sensing and geographical information system, in support of urban growth assessment. These SDSM findings along with current land use policies and socio-economic impact were included as either factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in megacities for 2030 and 2050. Urban expansion is expected in these megacities given the assumption of stationarity in urban growth process, although climate change impacts the land use changes and management. More land use protection should be addressed in order to alleviate the impact of climate change

Application of Markov Chain Model and ArcGIS in Land Use Projection of Ala River Catchment, Akure, Nigeria

Increase land use change is one of the consequences of rapid population growth of cities in developing countries with its negative consequences on the environment. This study generates previous and present land use of Ala watershed and project the future land use using Markov chain model and ArcGIS software (version 10.2.1). Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 used to generate land use (LU) and land cover (LC) images for the years 2000, 2010 and 2019. Six LU/LC classes were considered as follows: developed area (DA), open soil (OS), grass surface (GS), light forest (LF), wetland (WL) and hard rock (HR). Markov chain analysis was used in predicting LU/LC types in the watershed for the years 2029 and 2039. The veracity of the model was tested with Nash Sutcliffe Efficiency index (NSE) and Percent Bias methods. The model results show that the study area is growing rapidly particularly in the recent time. This urban expansion results in significant decrease of WL coverage areas and the significant increase of DA. This implies reduction in the available land for dry season farming and incessant flood occurrence. Keywords: Land cover, land use change, Markov chain, ArcGIS, watershed, urbanizatio

Using scenario modelling for adapting to urbanization and water scarcity: towards a sustainable city in semi-arid areas

Sustainable development on a global scale has been hindered by urbanization and water scarcity, but the greatest threat is from decision-makers ignoring these challenges, particularly in developing countries. In addition, urbanization is spreading at an alarming rate across the globe, affecting the environment and society in profound ways. This study reviews previous studies that examined future scenarios of urban areas under the challenges of rapid population growth, urban sprawl and water scarcity, in order to improve supported decision-making (SDM). Scholars expected that the rapid development of the urbanization scenario would cause resource sustainability to continually be threatened as a result of excessive use of natural resources. In contrast, a sustainable development scenario is an ambitious plan that relies on optimal land use, which views land as a limited and non-renewable resource. In consequence, estimating these threats together could be crucial for planning sustainable strategies for the long term. In light of this review, the SDM tool could be improved by combining the cellular automata model, water evolution and planning model coupled with geographic information systems, remote sensing and criteria analytic hierarchical process modelling. Urban planners could optimize, simulate and visualize the dynamic processes of land-use change and urban water, using them to overcome critical conditions

Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006 and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km2 and from 18.10 to 28.45 km2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared towards the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study

Linnade laienemine Eestis: seire, analüüs ja modelleerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneLinnade laienemine, mida iseloomustab vähese tihedusega, ruumiliselt ebaühtlane ja hajutatud areng linna piiridest välja. Kuna linnade laienemine muudab põllumajandus- ja metsamaid ning väikesed muutused linnapiirkondades võivad pikaajaliselt mõjutada elurikkust ja maastikku, on hädavajalik seirata linnade ruumilist laienemist ning modelleerida tulevikku, saamaks ülevaadet suundumustest ja tagajärgedest pikemas perspektiivis. Eestis võeti pärast taasiseseisvumist 1991. aastal vastu maareformi seadus ning algas “maa” üleandmine riigilt eraomandisse. Sellest ajast peale on Eestis toimunud elamupiirkondade detsentraliseerimine, mis on mõjutanud Tallinna ümbruse põllumajandus- ja tööstuspiirkondade muutumist, inimeste elustiili muutusi ning jõukate inimeste elama asumist ühepereelamutesse Tallinna, Tartu ja Pärnu lähiümbruse. Selle aja jooksul on Eesti rahvaarv vähenenud 15,31%. Käesoleva doktoritöö eesmärgiks on "jälgida, analüüsida ja modelleerida Eesti linnade laienemist viimase 30 aasta jooksul ning modelleerida selle tulevikku", kasutades paljusid modelleerimismeetodeid, sealhulgas logistilist regressiooni, mitmekihilisi pertseptronnärvivõrke, rakkautomaate, Markovi ahelate analüüsi, mitme kriteeriumi. hindamist ja analüütilise hierarhia protsesse. Töö põhineb neljal originaalartiklil, milles uuriti linnade laienemist Eestis. Tegu on esimese põhjaliku uuringuga Eesti linnade laienemise modelleerimisel, kasutades erinevaid kaugseireandmeid, mõjutegureid, parameetreid ning modelleerimismeetodeid. Kokkuvõtteks võib öelda, et uusehitiste hajumismustrid laienevad jätkuvalt suuremate linnade ja olemasolevate elamupiirkondade läheduses ning põhimaanteede ümber.Urban expansion is characterized by the low–density, spatially discontinued, and scattered development of urban-related constructions beyond the city boundaries. Since urban expansion changes the agricultural and forest lands, and slight changes in urban areas can affect biodiversity and landscape on a regional scale in the long-term, spatiotemporal monitoring of urban expansion and modeling of the future are essential to provide insights into the long-term trends and consequences. In Estonia, after the regaining independence in 1991, the Land Reform Act was passed, and the transfer of “land” from the state to private ownership began. Since then, Estonia has experienced the decentralization of residential areas affecting the transformation of agricultural and industrial regions around Tallinn, changes in people's lifestyles, and the settling of wealthy people in single-family houses in the suburbs of Tallinn, Tartu, and Pärnu. During this period, Estonia's population has declined dramatically by 15.31%. Therefore, this dissertation aims to "monitor, analyze and model Estonian urban expansion over the last 30 years and simulate its future" using many modeling approaches including logistic regression, multi-layer perceptron neural networks, cellular automata, Markov chain Analysis, multi-criteria evaluation, and analytic hierarchy process. The thesis comprises four original research articles that studied urban expansion in Estonia. So far, this is the first comprehensive study of modeling Estonian urban expansion utilizing various sets of remotely sensed data, driving forces and predictors, and modeling approaches. The scattering patterns of new constructions are expected to continue as the infilling form, proximate to main cities and existing residential areas and taking advantage of main roads in future.https://www.ester.ee/record=b550782

Geosimulation and Multicriteria Modelling of Residential Land Development in the City of Tehran: A Comparative Analysis of Global and Local Models

Conventional models for simulating land-use patterns are insufficient in addressing complex dynamics of urban systems. A new generation of urban models, inspired by research on cellular automata and multi-agent systems, has been proposed to address the drawbacks of conventional modelling. This new generation of urban models is called geosimulation. Geosimulation attempts to model macro-scale patterns using micro-scale urban entities such as vehicles, homeowners, and households. The urban entities are represented by agents in the geosimulation modelling. Each type of agents has different preferences and priorities and shows different behaviours. In the land-use modelling context, the behaviour of agents is their ability to evaluate the suitability of parcels of land using a number of factors (criteria and constraints), and choose the best land(s) for a specific purpose. Multicriteria analysis provides a set of methods and procedures that can be used in the geosimulation modelling to describe the behaviours of agents. There are three main objectives of this research. First, a framework for integrating multicriteria models into geosimulation procedures is developed to simulate residential development in the City of Tehran. Specifically, the local form of multicriteria models is used as a method for modelling agents’ behaviours. Second, the framework is tested in the context of residential land development in Tehran between 1996 and 2006. The empirical research is focused on identifying the spatial patterns of land suitability for residential development taking into account the preferences of three groups of actors (agents): households, developers, and local authorities. Third, a comparative analysis of the results of the geosimulation-multicriteria models is performed. A number of global and local geosimulation-multicriteria models (scenarios) of residential development in Tehran are defined and then the results obtained by the scenarios are evaluated and examined. The output of each geosimulation-multicriteria model is compared to the results of other models and to the actual pattern of land-use in Tehran. The analysis is focused on comparing the results of the local and global geosimulation-multicriteria models. Accuracy measures and spatial metrics are used in the comparative analysis. The results suggest that, in general, the local geosimulation-multicriteria models perform better than the global methods

Exploring the potential climate change impact on urban growth in London by a cellular automata-based Markov chain model

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Urbanization has become a global trend under the combined influence of population growth, socioeconomic development, and globalization. Even though recent urban planning in London has been more deliberate, the relationships between climate change and urban growth in the context of economic geography are still somewhat unclear. This study relies on rainfall prediction with the aid of the Statistical DownScaling Model (SDSM), which provides the statistical foundation for future flooding potential within the urban space of London while considering major socioeconomic policies related to land use management. These SDSM findings, along with current land use policies, were included as other factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in London for 2030 and 2050. Two scenarios with the inclusion and exclusion of flood impact factor, respectively, were applied to evaluate the impact of climate change on urban growth. Findings indicated: (1) mean monthly projected precipitation derived by SDSM is expected to increase for the year 2030 in London, which will affect the flooding potential and hence the area of open space; (2) urban and open space are expected to increase > 16 and 20km 2 (in percentage of 1.51 and 1.92 compared to 2012) in 2030 and 2050, respectively, while agriculture is expected to decrease significantly due to urbanization and climate change; (3) the inclusion of potential flood impact induced from the future precipitation variability drives the development toward more open space and less urban area.The research is supported by the Global Innovation Initiative (British Council Grant No. Gll206), funded by the British Council and the Department for Business, Innovation and Skills

State of the Art on Artificial Intelligence in Land Use Simulation

[Abstract] This review presents a state of the art in artificial intelligence applied to urban planning and particularly to land-use predictions. In this review, different articles after the year 2016 are analyzed mostly focusing on those that are not mentioned in earlier publications. Most of the articles analyzed used a combination of Markov chains and cellular automata to predict the growth of urban areas and metropolitan regions. We noticed that most of these simulations were applied in various areas of China. An analysis of the publication of articles in the area over time is included.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (ref. ED431G/01 and ED431D 2017/16), the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002 and UNLC13-13-3503), and the European Regional Development Funds (FEDER). CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia,” supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaria Xeral de Universidades” (grant no. ED431G 2019/01)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G 2019/0