CA Model Research Articles

A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects

Land use and land cover change (LUCC) simulation models are effective and reproducible tools for analyzing both the causes and consequences of future landscape dynamics under various scenarios. Current simulation models primarily focus on the evolution of specific land use types under the influence of human activities, but they rarely consider background climatic effects. However, these background climate changes significantly affect the landscape dynamics and should be incorporated into long-term LUCC simulations under various human-climate-included scenarios. In this paper, we propose a future land use simulation (FLUS) model that explicitly simulates the long-term spatial trajectories of multiple LUCCs. The top-down system dynamics and bottom-up cellular automata were interactively coupled during the projection period, which improved the model’s ability to accurately simulate future land use patterns. A self-adaptive inertia and competition mechanism was developed within the CA model to process the complex competitions and interactions between the different land use types. The proposed model was applied to an LUCC simulation in China from 2000 to 2010. The results show promising grid-to-grid agreement compared to actual land use, and the simulation accuracy is higher than other well-accepted models, such as CLUE-S and CA models. The model was further applied to the simulation of four scenarios from 2010 to 2050 that depict different development strategies by considering various socio-economic and natural climatic factors. The simulation results and findings demonstrate that the proposed model is effective for future LUCC simulation under variously designed scenarios. FLUS is available for free download at http://www.geosimulation.cn/FLUS.html.

A high-performance cellular automata model for urban simulation based on vectorization and parallel computing technology

ABSTRACTCellular automata (CA) models can simulate complex urban systems through simple rules and have become important tools for studying the spatio-temporal evolution of urban land use. However, the multiple and large-volume data layers, massive geospatial processing and complicated algorithms for automatic calibration in the urban CA models require a high level of computational capability. Unfortunately, the limited performance of sequential computation on a single computing unit (i.e. a central processing unit (CPU) or a graphics processing unit (GPU)) and the high cost of parallel design and programming make it difficult to establish a high-performance urban CA model. As a result of its powerful computational ability and scalability, the vectorization paradigm is becoming increasingly important and has received wide attention with regard to this kind of computational problem. This paper presents a high-performance CA model using vectorization and parallel computing technology for the computation-intensive and data-intensive geospatial processing in urban simulation. To transfer the original algorithm to a vectorized algorithm, we define the neighborhood set of the cell space and improve the operation paradigm of neighborhood computation, transition probability calculation, and cell state transition. The experiments undertaken in this study demonstrate that the vectorized algorithm can greatly reduce the computation time, especially in the environment of a vector programming language, and it is possible to parallelize the algorithm as the data volume increases. The execution time for the simulation of 5-m resolution and 3 × 3 neighborhood decreased from 38,220.43 s to 803.36 s with the vectorized algorithm and was further shortened to 476.54 s by dividing the domain into four computing units. The experiments also indicated that the computational efficiency of the vectorized algorithm is closely related to the neighborhood size and configuration, as well as the shape of the research domain. We can conclude that the combination of vectorization and parallel computing technology can provide scalable solutions to significantly improve the applicability of urban CA.

A cellular automata model for traffic flow based on kinetics theory, vehicles capabilities and driver reactions

In this paper, a reliable cellular automata model oriented to faithfully reproduce deceleration and acceleration according to realistic reactions of drivers, when vehicles with different deceleration capabilities are considered is presented. The model focuses on describing complex traffic phenomena by coding in its rules the basic mechanisms of drivers behavior, vehicles capabilities and kinetics, while preserving simplicity. In particular, vehiclés kinetics is based on uniform accelerated motion, rather than in impulsive accelerated motion as in most existing CA models. Thus, the proposed model calculates in an analytic way three safe preserving distances to determine the best action a follower vehicle can take under a worst case scenario. Besides, the prediction analysis guarantees that under the proper assumptions, collision between vehicles may not happen at any future time. Simulations results indicate that all interactions of heterogeneous vehicles (i.e., car–truck, truck–car, car–car and truck–truck) are properly reproduced by the model. In addition, the model overcomes one of the major limitations of CA models for traffic modeling: the inability to perform smooth approach to slower or stopped vehicles. Moreover, the model is also capable of reproducing most empirical findings including the backward speed of the downstream front of the traffic jam, and different congested traffic patterns induced by a system with open boundary conditions with an on-ramp. Like most CA models, integer values are used to make the model run faster, which makes the proposed model suitable for real time traffic simulation of large networks.

Hydrogen can alleviate post-cardiac arrest myocardium injury in rabbits

To investigate the effects of hydrogen (H2) on myocardium injury post-cardiac arrest (CA) in rabbits. Sixty New Zealand rabbits were randomly divided into H2 treatment group (n = 30) and control group (n = 30) by random number table. The rabbit CA model was established by means of electrical stimulation of external membrane, both groups were mechanically ventilated. Cardiopulmonary resuscitation (CPR) was performed after 6 minutes of nonintervention, and stopped after restoration of spontaneous circulation (ROSC). Inhalation of 2% H2 gas was conferred to rabbits immediately at the end of CA modeling for 72 hours in H2 treatment group. Air was given to rabbits in control group instead. The survival rate of rabbits was analyzed. Heart rate, ventricular premature beat frequency, and the levels of blood samples cardiac troponin I (cTnI), left ventricular ejection fraction (LVEF), B-type natriuretic peptide (BNP), and blood lactic acid (Lac) were collected before CA and after ROSC in all rabbits. Rabbits were sacrificed and microstructure injury was observed by electric microscope after ROSC 72 hours. There were 28 animals ROSC in both groups; the survival number in H2 treatment group was higher than that in control group at 72 hours after ROSC (number: 15 vs. 7, χ2 = 4.791, P = 0.029). In the early stage of ROSC, the heart rate of two groups slowed down, the number of premature ventricular increased, and then gradually recovered; the heart rate in H2 treatment group was returning to normal more quickly than that in control group at 48 hours after ROSC (bpm: 319±63 vs. 362±40, P < 0.05); the ventricular premature beat frequency was lower than that in control group at 72 hours after ROSC (times per minutes: 9.1±4.3 vs. 15.0±8.0, P < 0.05). The animals of two groups had different degrees of myocardial damage and cardiac insufficiency after ROSC, and restored with the extension of time. Compared with control group, the level of BNP in H2 treatment group was significant decreased at 24 hours after ROSC (ng/L: 385±98 vs. 488±174, P < 0.05), the levels of cTnI and Lac were significant decreased at 48 hours after ROSC [cTnI (μg/L: 1.83±0.68 vs. 2.83±0.98, Lac (mmol/L): 5.5±1.6 vs. 7.9±2.6, both P < 0.01], the LVEF was slightly higher than that at 72 hours after ROSC (0.690±0.040 vs. 0.650±0.041, P = 0.051). Compared with control group, less damage to myocardial ultra structure was found in H2 treatment group at 72 hours after ROSC. Inhalation of H2 alleviates cardiac dysfunction and myocardial injury after CPR.

Influence of inelastic collisions with hydrogen atoms on the non-LTE modelling of Ca i and Ca ii lines in late-type stars

We perform the non-local thermodynamic equilibrium (NLTE) calculations for Ca I-II with the updated model atom that includes new quantum-mechanical rate coefficients for Ca I + H I collisions from two recent studies, that is, by Barklem and by Mitrushchenkov, Guitou, Belyaev, Yakovleva, Spielfiedel, and Feautrier, and investigate the accuracy of calcium abundance determinations using the Sun, Procyon, and five metal-poor (MP) stars with well-determined stellar parameters. We show that both collisional recipes lead to very similar NLTE results. When using the subordinate lines of Ca I and the high-excitation lines of Ca II, NLTE provides the smaller line-to-line scatter compared with the LTE case for each star. For Procyon, NLTE removes a steep trend with line strength among strong Ca I lines seen in LTE and leads to consistent [Ca/H] abundances from the two ionisation stages. In the MP stars, the NLTE abundance from Ca II 8498 A agrees well with that from the Ca I subordinate lines. NLTE largely removes abundance discrepancies between the high-excitation lines of Ca I and Ca II 8498 A obtained for our four [Fe/H] < -2 stars under the LTE assumption. We investigate the formation of the Ca I resonance line in the [Fe/H] < -2 stars. Consistent NLTE abundances from the Ca I resonance line and the Ca II lines are found for two hyper metal-poor stars HE0107-5240 and HE1327-2326. We provide the NLTE abundance corrections for 28 lines of Ca I in a grid of model atmospheres suitable for abundance analysis of FGK-type dwarfs and subgiants.

Pedestrian counter flow in discrete space and time: experiment and its implication for CA modelling

ABSTRACTCellular automaton (CA) approach has been widely used in pedestrian flow studies. However, one problem of CA approach is that gridlock usually occurs in counter flow. One possible reason is that CA models still do not represent correctly cognitive processes of real people. To study whether the gridlock would occur when real people were required to walk in discrete space and time, we performed an experimental study on pedestrian counter flow. Remarkable collaboration behaviours of pedestrians have been observed, which enable the formation of exit rows and prevent the formation of gridlock. Furthermore, we performed a comparative experiment in which pedestrians walked under normal condition. Our studies indicate that to fully describe pedestrian counter flow with CA approach, the remarkable collaboration behaviours of pedestrians need to be considered. Moreover, one might also need to consider other factors such as flexible characteristics of pedestrians, small cell size, and various walk speeds.

Effects of rainy weather on traffic accidents of a freeway using cellular automata model**Project supported by the National Natural Science Foundation of China (Grant No. 50478088) and the Natural Science Foundation of Hebei Province, China (Grant No. E2015202266)

The aim of this work is to investigate the influence of rainy weather on traffic accidents of a freeway. The micro-scale driving behaviors in rainy weather and possible vehicle rear-end and sideslip accidents are analyzed. An improved CA model of two lanes one-way freeway is presented, where some vehicle accidents will occur when the necessary conditions are simultaneously satisfied. The characteristics of traffic flow under different rainfall intensities are discussed and the accident probabilities are analyzed via the simulation experiments by using variable speed limit (VSL) and incoming flow control. The results indicate that the measures are effective especially during heavy rainstorms or short-time heavy rainfall. According to different rainfall intensities, an appropriate strategy should be adopted in order to reduce the probability of vehicle accidents and enhance traffic flux as well.

HMGB1 binding heptamer peptide improves survival and ameliorates brain injury in rats after cardiac arrest and cardiopulmonary resuscitation

Excessive inflammatory response produced after cardiac arrest and cardiopulmonary resuscitation (CA/CPR) is one of major causes of cerebral injury. High mobility group box 1 (HMGB1) is a pro-inflammatory cytokine and its role in brain injury after CA/CPR is unclear. Herein we investigated whether blocking HMGB1 signaling could ease brain injury after CA/CPR. Male Sprague–Dawley rats (n=181) were subjected to 8-min Asphyxia CA model or Sham operation. The ELISA data revealed both resuscitated patients and animals had elevated HMGB1 level in sera, compared with the healthy volunteers or Sham operative rats, respectively (P<0.01). Rats successfully resuscitated from CA were then randomly treated with either membrane permeable (TAT-fused) HMGB1 binding heptamer peptide (HBHP) or Scramble peptide. Results showed that HBHP treatment markedly improved 7-day survival rate, reduced neurological deficit scores, and prevented neuronal and dendrite loss in hippocampal CA1 region. Moreover, HBHP inhibited the activation of microglia and astrocytes and downregulated the mRNA and protein expressions of proinflammatory factors. We finally blocked toll-like receptor-4 (TLR4, one of HMGB1 receptors) with a specific antagonist TAK-242 before CA induction to confirm the detrimental effect of HMGB1 signaling and found blocking TLR4 could also attenuate the neuronal degeneration, as well as reduce NF-κB-mediated inflammatory signaling. Our findings indicate that CA/CPR can induce HMGB1 release to serum, while blocking HMGB1 signaling with peptide may improve the survival and attenuate post-resuscitation brain injury in the rat model of CA/CPR. TLR4 antagonist may also offer neuroprotective effects through weakening HMGB1-mediated proinflammatory reactions.

Decentralized estimation of forest fire spread using reactive and cognitive mobile sensors

This paper presents a study of the ability to build an observer for a complex system using a decentralized multi-agent system for the coordination of mobile sensors. The environment is modeled using a CA model representing forest fire spread. The initial distribution for the different species in the vegetation is generated using a Perlin algorithm. Implementation is realized on GPGPU. A coherence measure for the observation error is defined. The observation itself is realized with mobile sensors and a decentralized coordination of the trajectories. We analyze the balance between individual and collective behaviours of agents which is required to achieve the best performance with respect to the chosen coherence measure. Two kinds of agent’ behaviour are studied: reactive and cognitive.

Predicting future urban impervious surface distribution using cellular automata and regression analysis

Urban impervious surfaces are considered as key indicator of urbanization intensity and environmental quality. Due to their significant impact on surface runoff, flood frequency, and water quality, impervious surfaces have been identified as an important indicator for examining the hydrological impact of urbanization. The amount and distribution of impervious surfaces have been estimated using remote sensing and geographic information system (GIS) techniques. Little research, however, has been conducted to predict future impervious surface distributions. To address this problem, we developed an integrated residential/commercial growth and impervious surface distribution model to predict urban impervious surface distribution. Taking Milwaukee River Basin, Wisconsin as a case study, we simulated future residential and commercial developments using a CA model. Further, we developed a linear regression model to predict impervious surface distributions in residential and commercial land uses. Analysis of results suggests that the proposed model performs significantly better than the traditional approaches.

Two-Dimensional Chemical Etching Process Simulation for Printed Circuit Heat Exchanger Channels Based on Cellular Automata Model

ABSTRACTAs a new type of heat exchanger, the printed circuit heat exchanger (PCHE) with high compactness can work under high pressure and high temperature, and can be applied to the very high temperature gas nuclear reactors. The chemical etching technology is one of main processes during PCHE fabrication, which determines whether PCHE flow channels can be properly produced according to design. However, many factors can affect the etching quality and the poor etching process can cause a great deal of uncertainty on the precision of stainless steel flow channels. In this paper, a two-dimensional (2-D) cellular automata model (CA) is used to simulate the etching process on stainless steel to examine the change of channel features varying with time under certain etching rates. Moreover, the simulation results are compared with our previous experimental etching data. On the one hand, from the comparison of the simulation results with channels data by spray etching, 2-D CA model can realize the simulation of etching process and display channel features under different etching rates and lateral erosion rates in every time period. On the other hand, from the comparison of the simulation results with channels data by immersion etching under different FeCl3 concentration and etching liquid temperature, 2-D CA model vividly presents channel features under different factors, which can easily and clearly explain the influence of factors on the etching quality, puddling effect and so on. Therefore, the simulation result strengthens the understanding of the etching mechanism and is helpful to realize an efficient and precise PCHE fabrication.

Research on microstructure evolution of austenitization in grinding hardening by cellular automata simulation and experiment

Heat generated within the grinding zone is used to generate a hardened layer in grinding hardening. The phase fraction and carbon content of the hardened layer have significant effects on its mechanical properties. Specially, the heating rate is extremely high and the duration time beyond A c1 (austenitization starting temperature) is short in grinding hardening, so the austenitization is sometimes incompleted and the carbon content is always non-uniform. In the present paper, three-dimensional finite element method (FE) incorporating a regular triangle heat source is used for computing temperature histories of the specimen. Microstructure evolution model of austenitization of AISI 1045 steel considering the heating rate in grinding hardening is developed by using the cellular automata method (CA). In the CA model, austenite nucleation, pearlite dissolution, austenite growth in proeutectoid ferrite, and carbon diffusion are considered and simulated based on their respective theories. A new variable t d (duration time beyond A c1) is defined to describe the temperature history characteristic. The effects of grinding parameters on the temperature characteristic variables are studied. Meanwhile, austenitization are simulated under different grinding parameters. The microstructure of the hardened layer is observed in grinding hardening experiment to validate the CA model. The results show that, firstly, the CA model can effectively simulate the microstructure evolution. Secondly, both peak temperature and t d have significant effects on the austenitization in grinding hardening. Thirdly, better microstructure and mechanical properties can be obtained by optimizing grinding parameters.

Reorienting paradoxical land use policies towards coherence: A self-adaptive ensemble learning geo-simulation of tea expansion under different scenarios in subtropical China

The expansion of cash crops has raised contradicting interests between two bureaucratic bodies (the economy-oriented one that advocates cash crop production and the conservation-oriented one that focuses on natural resources protection) in many places around the world. Recent past has saw growing efforts on the theoretical linkages between cash crop production and conservation, but the solutions to the cash cropping −related land use conflicts remain as violent controversy. Using a geo-simulation approach, this paper models the tea expansion under different policy scenarios and evaluates the effectiveness of these policies in Anji County (China), as a contribution to the scientific basis for formulating sustainable cash cropping practices and alternative land use policies. In particular, a new self-adaptive cellular automaton model based on ensemble learning (EL-CA) is developed and three policy scenarios (economy-over-conservation (EOC), conversion-over-economy (COE), and economy-balance-conservation (EBC)) are set to predict the tea expansion patterns in 2025. Results show that the EL-CA model significantly outperforms the traditional CA models based on empirical statistics. We find that the tea expansion under the EOC scenario is much more intensive than that under the COE and EBC scenarios. The most outstanding ecological consequence of tea expansion is the occupation of forests. Employing an equivalent coefficient approach, we further quantify the trade-offs between economic incomes (from tea expansion) and ecological loss (due to ecosystem service value (ESV) declines) under the three policy scenarios. In the EOC scenario, the loss in ESV far exceeds the benefit of tea expansion. Net change of ESV is higher than that of economic return under the COE. The economic benefit is approximately equal to the ecological loss in the EBC scenario. The EBC should be a socially preferred scenario, since it leads to sustainable tea expansion and minimal ecological impacts. Though the EBC scenario is a desirable choice, how to enforce these policies is an important consideration. Given the complexity in the Chinese policy context, we finally propose several possible measures to promote the coherence of paradoxical policies involving the allocation of land for cash crop cultivation.

Rheological characterisation and modelling of bitumen containing reclaimed components

ABSTRACTCurrently, many efforts are undertaken to reach asphalt performance from binder performance in order to reduce costly and time consuming laboratory tests on asphalt materials. In this context, it is important to use and develop appropriate testing methods for a comprehensive binder characterisation, which requires suitable material models. Depending on the application, different models can be used to model rheological bitumen behaviour. This paper focuses on the applicability of the Christensen and Anderson model (CA model) and the Olard-Di Benedetto (2S2P1D) model for bitumen samples containing different amounts of recycled components using various objective functions in the optimisation process. The purpose is to analyse the influence of the reclaimed asphalt rate on the parameters of the investigated models. Dynamic Shear Rheometer tests are conducted on binder samples extracted from Stone Mastic Asphalt mixes with polymer modified bitumen and Asphalt Concrete mixes with paving bitumen, each containing 0–100% reclaimed material components. The resulting influences of the recycled materials are illustrated through master curves, black diagrams and Cole–Cole plots by fitting these experimental data by the application of 2S2P1D and CA model.

Modelling vehicular interactions for heterogeneous traffic flow using cellular automata with position preference

This paper proposes and validates a modified cellular automata model for determining interaction rate (i.e. number of car-following/overtaking instances) using traffic flow data measured in the field. The proposed model considers lateral position preference by each vehicle type and introduces a position preference parameter β in the model which facilitates gradual drifting towards preferred position on road, even if the gap in front is sufficient. Additionally, the model also improves upon the conventional model by calculating safe front and back gap dynamically based on speed and deceleration properties of leader and follower vehicles. Sensitivity analysis was carried out to determine the effect of β on vehicular interactions and the model was calibrated and validated using interaction rates observed in the field. Paired tests were conducted to determine the validity of the model in determining interaction rates. Results of the simulations show that there is a parabolic relationship between area occupancy and interaction rate of different vehicle types. The model performed satisfactorily as the simulated interaction rate between different vehicle types were found to be statistically similar to those observed in field. Also, as expected, the interaction rate between light motor vehicles (LMVs) and heavy motor vehicles (HMVs) were found to be higher than that between LMVs and three wheelers because LMVs and HMVs share the same lane. This could not be done using conventional CA models as lateral movement rules were dictated by only speeds and gaps. So, in conventional models, the vehicles would end up in positions which are not realistic. The position preference parameter introduced in this model motivates vehicles to stay in their preferred positions. This study demonstrates the use of interaction rate as a measure to validate microscopic traffic flow models.

Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules

ABSTRACTA novel generalized pattern search (GPS)-based cellular automata (GPS-CA) model was developed to simulate urban land-use change in a GIS environment. The model is built on a fitness function that computes the difference between the observed results produced from remote-sensing images and the simulated results produced by a general CA model. GPS optimization incorporating genetic algorithms (GAs) searches for the minimum difference, i.e. the smallest accumulated residuals, in fitting the CA transition rules. The CA coefficients captured by the GPS method have clear physical meanings that are closely associated with the dynamic mechanisms of land-use change. The GPS-CA model was applied to simulate urban land-use change in Kunshan City in the Yangtze River Delta from 2000 to 2015. The results show that the GPS method had a smaller root mean squared error (0.2821) than a logistic regression (LR) method (0.5256) in fitting the CA transition rules. The GPS-CA model thus outperformed the LR-CA model, with an overall accuracy improvement of 4.7%. As a result, the GPS-CA model should be a superior tool for modeling land-use change as well as predicting future scenarios in response to different conditions to support the sustainable urban development.

Trifocal constructive approach in developing coders, simulated, and hardware implemented, for the introduction of coding theory

AbstractConstructive approach (CA) for increased efficiency of engineering education has been receiving growing attention and wider application in engineering education in recent years. New approach combining CA and Unified Process Model (UPM) for developing of software simulation packages and hardware is developed and proposed, on this way novel trifocal approach to engineering is proposed and discussed. Through this approach students developed virtual simulators as software prototype for hardware implementation of coders. This approach increased learning and understanding of mathematical and theoretical basics of coding theory, which is integral part of digital communication systems. For implementing construction of software simulator and hardware coders in the curricula we developed multidisciplinary approach. Joint and interconnected lectures were developed for the courses of digital communication and software engineering. For validation of developed software packages and constructed hardware coders used laboratory facilities for digital electronics. Trends of the level of understanding and result achieved on the exams showed that implementation of this approach provided better results in solving mathematical problems in the field of coding theory.

Cellular automata model for human articular chondrocytes migration, proliferation and cell death: An in vitro validation.

Articular cartilage is characterized by low cell density of only one cell type, chondrocytes, and has limited self-healing properties. When articular cartilage is affected by traumatic injuries, a therapeutic strategy such as autologous chondrocyte implantation is usually proposed for its treatment. This approach requires in vitro chondrocyte expansion to yield high cell number for cell transplantation. To improve the efficiency of this procedure, it is necessary to assess cell dynamics such as migration, proliferation and cell death during culture. Computational models such as cellular automata can be used to simulate cell dynamics in order to enhance the result of cell culture procedures. This methodology has been implemented for several cell types; however, an experimental validation is required for each one. For this reason, in this research a cellular automata model, based on random-walk theory, was devised in order to predict articular chondrocyte behavior in monolayer culture during cell expansion. Results demonstrated that the cellular automata model corresponded to cell dynamics and computed-accurate quantitative results. Moreover, it was possible to observe that cell dynamics depend on weighted probabilities derived from experimental data and cell behavior varies according to the cell culture period. Thus, depending on whether cells were just seeded or proliferated exponentially, culture time probabilities differed in percentages in the CA model. Furthermore, in the experimental assessment a decreased chondrocyte proliferation was observed along with increased passage number. This approach is expected to having other uses as in enhancing articular cartilage therapies based on tissue engineering and regenerative medicine.

Two-Stage Prediction on Effects of Mixtures Containing Phenolic Compounds and Heavy Metals on Vibrio qinghaiensis sp. Q67.

Two-stage prediction (TSP) model had been developed to predict toxicities of mixtures containing complex components, but its prediction power need to be further validated. Six phenolic compounds and six heavy metals were selected as mixture components. One mixture (M1) was built with equivalent-effect concentration ratio and four mixtures (M2-M5) were designed with fixed concentration ratio. In M1-M5, the toxicities were well predicted by TSP model, while CA overestimated and IA underestimated the toxicities. In M1-M5, compared with the actual mixture EC50 value, the prediction errors of TSP model (13.9%, 17.9%, 19.2%, and 17.3% and 15.8%, respectively) were significantly lower than those in the CA (higher than 30%) and IA models (20.9%, 33.0%, 20.6%, 21.8% and 12.5%, respectively). Thus, the TSP model performed better than the CA and IA model.

A CA-based land system change model: LANDSCAPE

ABSTRACTCellular automata (CA) models are widely used to simulate land-use changes because of their simplicity, flexibility, intuitiveness and ability to incorporate the spatial and temporal dimensions of processes. A small number of CA-based models have been developed to simulate changes in multiple land uses, most of which use the hierarchical allocation strategy and/or inertia factors to enable these CA models to do so accurately. However, only some of these models allow explicit determination of the allocation sequence for active land uses according to the hierarchical allocation strategy and the objective calculation of inertia factors. In this paper, we proposed a CA-based model, i.e. the LAND System Cellular Automata model for Potential Effects (LANDSCAPE), with a hierarchical allocation strategy and resistances, to simulate changes in multiple land uses. Furthermore, we introduced effective ways to objectively determine the allocation sequence for active land uses and calculate resistances for individual land uses. The results show that the LANDSCAPE model, with a calibrated allocation sequence and resistances, is reliable and accurate for simulating multiple land-use changes.