A novel spatio-temporal cellular automata model coupling partitioning with CNN-LSTM to urban land change simulation

Aburas, 2016, The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: a review, Int. J. Appl. Earth Observ. Geoinf., 52, 380 Ahmadi-Sani, 2022, Effect of land-use change on runoff in Hyrcania, Land (Basel), 11, 220 Akram, R. et al. (2018). Paddy land pollutants and their role in climate change. In: Hashmi, M., Varma, A. (eds) Environmental Pollution of Paddy Soils. Soil Biology, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-319-93671-0_7. Bahmani, 2021, Geographically weighted regression hybridized with Kriging model for delineation of drought-prone areas, Environ. Model. Assess., 26, 803, 10.1007/s10666-021-09789-z Barreira-González, 2017, Configuring the neighbourhood effect in irregular cellular automata based models, Int. J. Geogr. Inf. Sci., 31, 617, 10.1080/13658816.2016.1219035 Barreira-González, 2015, From raster to vector cellular automata models: a new approach to simulate urban growth with the help of graph theory, Comput., Environ. Urban Syst.,, 54, 119, 10.1016/j.compenvurbsys.2015.07.004 Chen, 2022, Delimitation of urban development boundary and construction of space control system from the perspective of territorial spatial planning, J. Geo-inf. Sci., 24, 263 Chen, 2019, Simulating urban growth boundaries using a patch-based cellular automaton with economic and ecological constraints, Int. J. Geogr. Inf. Sci., 33, 55, 10.1080/13658816.2018.1514119 Fahad, S. et al. (2019). Drought tolerance in plantsrole of phytohormones and scavenging system of ROS. Plant Tolerance to Environmental Stress, 103–114. https://doi.org/10.1201/9780203705315-7. Feng, 2017, Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change, Environ. Monit. Assess., 189, 1, 10.1007/s10661-017-6224-8 Gounaridis, 2019, A random forest-cellular automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales, Sci. Total Environ., 646, 320, 10.1016/j.scitotenv.2018.07.302 Guan, 2019, Dynamic simulation of land use change based on logistic-CA-Markov and WLC-CA-Markov models: a case study in three gorges reservoir area of Chongqing, China, Environ. Sci. Pollut. Res. Int., 26, 20669, 10.1007/s11356-019-05127-9 Guo, 2020, Monitoring and simulation of dynamic spatiotemporal land use/cover changes, Complexity, 77060 Jin, 2020, Multi-task learning model based on multi-scale CNN and LSTM for sentiment classification, IEEE Access., 8, 77060, 10.1109/ACCESS.2020.2989428 Karimi, 2019, An enhanced support vector machine model for urban expansion prediction, Comput., Environ. Urban Syst., 75, 61, 10.1016/j.compenvurbsys.2019.01.001 Li, 2022, Firefly algorithm-based cellular automata for reproducing urban growth and predicting future scenarios, Sustain. Cities Soc., 76, 10.1016/j.scs.2021.103444 Li, 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, J. Environ. Manag., 287, 10.1016/j.jenvman.2021.112321 Liang, 2021, Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: a case study in Wuhan, China, Comput. Environ. Urban Syst., 85, 10.1016/j.compenvurbsys.2020.101569 Liu, 2021, Modeling the response of ecological service value to land use change through deep learning simulation in Lanzhou, China, Sci. Total Environ., 796, 10.1016/j.scitotenv.2021.148981 Liu, 2021, Simulation of dynamic urban expansion under ecological constraints using a long short term memory network model and cellular automata, Remote Sens. (Basel), 13, 1499, 10.3390/rs13081499 Lyu, 2019, The impact of urbanization and climate change on ecosystem services: a case study of the city belt along the Yellow River in Ningxia, China, Comput. Environ. Urban Syst., 77, 10.1016/j.compenvurbsys.2019.101351 Mustafa, 2018, Comparing support vector machines with logistic regression for calibrating cellular automata land use change models, Eur. J. Remote Sens., 51, 391, 10.1080/22797254.2018.1442179 Mwabumba, 2022, Analysis of land use and land-cover pattern to monitor dynamics of Ngorongoro world heritage site (Tanzania) using hybrid cellular automata-Markov model, Curr. Res. Environ. Sustain., 4, 10.1016/j.crsust.2022.100126 Napoleon, 2011, A new method for dimensionality reduction using k-means clustering algorithm for high dimensional data set, Int. J. Comput. Appl., 13, 41 Noszczyk, 2018, A review of approaches to land use changes modeling, Hum. Ecol. Risk Assess.: Int. J., 25, 1377, 10.1080/10807039.2018.1468994 Qian, 2020, Coupling cellular automata with area partitioning and spatiotemporal convolution for dynamic land use change simulation, Sci. Total Environ., 722, 10.1016/j.scitotenv.2020.137738 Ren, 2019, Spatially explicit simulation of land use/land cover changes: current coverage and future prospects, Earth Sci. Rev., 190, 398, 10.1016/j.earscirev.2019.01.001 Shafizadeh-Moghadam, 2019, Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches, Comput. Environ. Urban Syst., 76, 91, 10.1016/j.compenvurbsys.2019.04.005 Sourn, 2022, Assessment of land use and land cover changes on soil erosion using remote sensing, GIS and RUSLE model: a case study of Battambang Province, Cambodia, Sustainability, 14, 4066, 10.3390/su14074066 Sönmez, 2016, The effects of sulfur, cattle, and poultry manure addition on soil phosphorus, Turk. J. Agric. For., 40, 6 Sun, 2018, An improved boosting learning saliency method for built-up areas extraction in Sentinel-2 images, Remote Sens. (Basel), 10, 1863, 10.3390/rs10121863 Takamatsu, 2014, Development of a land-use forecast tool for future water resources assessment: case study for the Mekong River 3S sub-basins, Sustain. Sci., 9, 157, 10.1007/s11625-013-0225-5 Truong, 2022, A land-use change model to support land-use planning in the Mekong delta (MEKOLUC), Land (Basel), 11, 297 Turan, 2019, Co-inoculation effect of Rhizobium and Achillea millefolium L. oil extracts on growth of common bean (Phaseolus vulgaris L.) and soil microbial-chemical properties, Sci. Rep., 9, 15178, 10.1038/s41598-019-51587-x Turan, V., Aydın, S., Sönmez, O. (2022). Production, cost analysis, and marketing of bioorganic liquid fertilizers and plant nutrition enhancers. In: Amaresan, N., Dharumadurai, D., Cundell, D.R. (eds) Industrial Microbiology Based Entrepreneurship. Microorganisms for Sustainability, vol 42. Springer, Singapore. https://doi.org/10.1007/978-981-19-6664-4_13. Vani, 2022, Modelling urban expansion of a south-east Asian city, India: comparison between SLEUTH and a hybrid CA model, Model. Earth Syst. Environ., 8, 1419, 10.1007/s40808-021-01150-3 Wang, 2021, Simulating urban land growth by incorporating historical information into a cellular automata model, Landsc. Urban Plan., 214, 10.1016/j.landurbplan.2021.104168 Wang, 2019, Predicting land use changes in northern China using logistic regression, cellular automata, and a Markov model, Arab. J. Geosci., 12, 1, 10.1007/s12517-019-4985-9 Wang, 2022, Dominant transition probability: combining CA-Markov model to simulate land use change, Environ., Dev. Sustain., 1 Wu, 2021, Simulating spatiotemporal land use change in middle and high latitude regions using multiscale fusion and cellular automata: the case of Northeast China, Ecol. Indic., 133, 10.1016/j.ecolind.2021.108449 Xing, 2020, A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation, Comput. Geosci., 137, 10.1016/j.cageo.2020.104430 Yao, 2017, Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata, Int. J. Geogr. Inf. Sci., 31, 2452, 10.1080/13658816.2017.1360494 Zhai, 2020, Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata, Int. J. Geogr. Inf. Sci., 34, 1475, 10.1080/13658816.2020.1711915 Zhang, 2020, A novel model integrating deep learning for land use/cover change reconstruction: a case study of Zhenlai county, northeast china, Remote Sens. (Basel), 12, 3314, 10.3390/rs12203314 Zhang, 2022, The effects of sample size and sample prevalence on cellular automata simulation of urban growth, Int. J. Geogr. Inf. Sci., 36, 158, 10.1080/13658816.2021.1931237 Zhang, 2021, A hybrid deep learning technology for PM2. 5 air quality forecasting, Environ. Sci. Pollut. Res., 28, 39409, 10.1007/s11356-021-12657-8 Zhou, 2022, Urban expansion simulation and development-oriented zoning of rapidly urbanising areas: a case study of Hangzhou, Sci. Total Environ., 807, 10.1016/j.scitotenv.2021.150813