Improving SLEUTH Calibration with a Genetic Algorithm

Keith C. Clarke

Department of Geography, University of California, Santa Barbara, Santa Barbara CA 93106-4060, U.S.A.

Keywords: Land Use Change, Model, SLEUTH, Calibration, Cellular Automata, Genetic Algorithm.

Abstract: A review of calibration methods used for cellular automaton models of land use and land cover change was

performed. Calibration advances have been achieved through machine learning algorithms to either extract

land change rules, or optimize model performance. Many models have now automated the calibration

process, reducing the need for subjective choices. Here, the brute force calibration procedure for the

SLEUTH CA-based land use change model was replaced with a genetic algorithm (GA). The GA

calibration process populates a “chromosome” with five parameter combinations (genes). These

combinations are then used for model calibration runs, and the most successful selected for mutation, while

the least successful are replaced with randomly selected values. Default values for the constants and rates of

the genetic algorithm were selected from SLEUTH applications. Model calibrations were completed using

both brute force calibration and the GA. The GA model performed as well as the brute force method, but

used vastly less computation time with speed up of about 3 to 22. The optimal values for GA calibration are

set as the defaults for SLEUTH-GA, a new version of the model. This paper is a contraction of Clarke (in

press), which reports on the full set of results.

1 INTRODUCTION

Land use change is driven by the conversion of

natural lands to agriculture, and increasingly by the

expansion of built-up land. Cities expand impervious

surfaces outward and inward and create other land

use changes at a distance. Land use and land cover

change modeling attempts to simulate these changes,

and asks how they can be modified, diverted or

prevented so that future cities are more sustainable.

Modeling can seek to gain an understanding of

a process, usually as revealed by spatial forms

(Clarke 2014a). Modeling seeks to forecast a

process, and so predict where and when changes will

occur (NRC 2014). Models allow exploration of

alternative futures by varying the forecasts to

embody different anticipated circumstances (Xiang

and Clarke 2003; Houet et al. 2016). A model can

also help others understand the process, its outcomes

and its consequences, and so educate. These

purposes are dependent on the accuracy, reliability

and effectiveness of the model.

Good models make their assumptions about a

process explicit, use facts and data as inputs, then

create accurate forecasts of future system states. To

be accurate, models must use real data to fine tune

the controls that create model behavior. The model

design should make careful choices of constants and

variables; and the model should use hindcasting, that

is, be applied to historical data to effectively

replicate the present. Accuracy can then be assessed

as the level of agreement between the forecast and

the actual (Pontius et al. 2007). The model’s level of

accuracy, reliability and effectiveness can then be

measured and optimized. This stage is called model

calibration, and calibration remains the most critical

phase of model design and application.

2 CALIBRATION

Calibration uses a vast array of tools and techniques

to optimize a model and seeks to determine the

impacts of changes in a specific constant or variable

upon the model outputs. Constants are the values

that remain internal to the model, and may be

choices of particular values or more structural

elements of the model. The determination of

constants is the first stage of calibration during

model design. Methods include inspection of the

correspondence of outputs, match statistics and the

computation of many outputs across a range of

Clarke, K.

Improving SLEUTH Calibration with a Genetic Algorithm.

DOI: 10.5220/0006381203190326

In Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017), pages 319-326

ISBN: 978-989-758-252-3

319

constant values. Critical in calibration are threshold

values, where a small change in the constant

produces large differences in the output--what Houet

et al. (2016) call “non path-dependent” and

contrasted/breaking trends. Simple models avoid

these values, while complex systems models exploit

them. Crossing these thresholds is called phase

change in complexity theory, and leads to

emergence (Holland 1998).

Calibration also involves repeated application

of the model, the measurement of model

performance, degree of fit, and the adjustment of

input variables and data until the performance is

maximized. This may involve accuracy of the model

outputs as measured using historical data, or

achievement of some other goal. A model is started

at some point in the past, and executed without

further input until the last period of known data (the

present), periodically matching its numerical and

spatially distributed outputs with real data.

Given the matches described above, measures

can be compiled that represent multiple performance

parameters. Changing parameters and repeating the

model application allows retention of the best

performing settings. One way to optimize is to

repeat the parameter changes for all possible

combinations and permutations of their values, so-

called brute force. Models increasingly use machine

learning algorithms to optimize. For example,

weights assigned in agent based models can be

selected using support vector machines, or cellular

automata behavior rules selected using genetic

algorithms (Clarke 2014b). Good calibrations derive

the best set of input parameters that determine the

model’s performance, accuracy and behavior. Good

models are always well calibrated.

Models of land use and land cover change have a

vast literature, with periodical reviews and surveys

of the models and their applications (NRC 2014).

All land change models require calibration, but these

calibrations are a function of the model type and its

intended purpose. A subset of land use change

models is cellular automata (CA) models, discussed

at length (Torrens and O’Sullivan 2001) and divided

into types (Sante et al. 2010). This short paper

focuses on CA models only, then a particular model

and its improvement using a genetic algorithm (GA)

to replace its current brute force calibration method.

An advantage of this approach is that it removes

human interaction and judgement entirely from the

calibration process (Jafarnezhad et al. 2015).

2.1 Cellular Automata Models

CA models are complex system models consisting

of: (1) a set of mutually exclusive and non-

overlapping states; (2) a framework of points, cells

or a grid in which each element is in one and only

one state; (3) a defined neighborhood, consisting of

a set of cells usually surrounding or adjacent to a

cell; (4) a set of rules that govern state changes as a

function of the other states within the neighborhood;

(5) a relation to discrete time, such that all cells are

evaluated in each time step; and (6) an initial

arrangement of the states within each of the cells.

In CA land use change models, the states are

standard land use classes, such as forest, agriculture,

urban and wetlands; the framework is a map, a grid

of raster cells within a GIS; the neighborhood is the

adjacent cells of the Moore, Von Neumann or other

neighborhood; the time steps are annual increments

from a start time to a stop time; and the initial

arrangements are mapped distributions at some point

in past time. The rules are determined during the

model design stage by following those of other

models, using some a priori assumption about

system behavior, derived statistically using

probabilities or from exogenous quotas, or derived

from data mining of past land use changes as

functions of location, type and quantity.

The rule sets associated with land use and land

cover change are often chosen by analysis of the

driving factors of land use change. The factors that

prove significant are then prioritized and assigned

weights. Modeling then consists of taking an input

model, combining the weighed input factors,

deciding probabilistically whether a change from

type A to type B could occur, then enacting the

change at the most probable locations.

2.2 Calibrating CA Models

Using two land use maps as inputs to derive a rule

set for CA by data mining has led to numerous

attempts to calibrate CA models with data reduction

methods. These include multi-criterion evaluation

(MCE) (Wu and Webster 1998), multi-objective

optimization (Cao et al 2014), logistic regression

(Wu 2002) and decision trees (Li and Yeh 2004).

Most successful among these methods have been

neural networks (Yang and Li 2007). Some models

use neural networks as the entire basis for land use

change modeling (e.g. ANN-CA by Li and Gar-On

Yeh 2002; and LTM by Pijanowski et al. 2002).

Other machine-learning algorithms have been

used to help calibrate (and derive CA rules for) CA

GAMOLCS 2017 - International Workshop on Geomatic Approaches for Modelling Land Change Scenarios

320

models of land use and land cover change. Long et

al. (2009), Hu and Lo (2007) and Liu and Phinn

(2003) used logistic regression to select CA

transition rules in the model design stage. Guan et al.

(2005) used artificial neural networks for the same

purpose. Another method is the support vector

machine (Yang et al. 2008). Others have used neural

networks to optimize CA control parameters (Li and

Yeh 2004). More recently, such methods as particle

swarm optimization (Feng et al. 2011) and ensemble

learning strategies (multiple methods in parallel)

have also been introduced (Gong et al. 2012).

Among the most successful machine learning

methods for CA rule selection and parameterization

are genetic algorithms (GA). A GA is a method for

solving optimization problems based on a process of

natural selection that mimics evolution in plants and

animals. The algorithm starts with an approximate

initial set of solutions, and then repeatedly modifies

the population of genes while assessing fitness. Each

iteration, changes are made to create better solutions

(evolution and mutation) and to allow new random

solutions that may outperform the current best

“gene.” Studies that have used GA to calibrate CA

include Colonna et al. (1998), Goldstein (2004),

Yang and Li (2007), Yang et al. (2008), Shan et al.

(2008), Cao et al. (2011), Feng and Liu (2012),

Clarke-Lauer and Clarke (2011), Garcia et al. (2013)

and Jafarnezhad et al. (2015).

There are many possible measures of goodness

of fit between a real map and a modeled map (fitness

of the gene or chromosome), including producers

and users accuracy, various Kappa measures,

matching of landscape metrics, correlation, the

Receiver Operating Characteristics curve and others.

Many calibrations simply use the percent correct as

a measure. As an example, the SLEUTH model

produces 13 regression-based fit measures, which in

the past were combined by multiplication, although

many studies have used the Lee-Sallee metric alone

(Silva and Clarke 2002). Current practice uses the

Optimal SLEUTH Metric (OSM) (Dietzel and

Clarke 2007). This measure uses a subset of 7 of the

13 metrics, also combined by multiplication,

selected to reduce interdependencies among the 13

metrics. The study reported here used the OSM as

the fitness measure for calibrating SLEUTH.

Use of GA implies creation of the equivalent of a

chromosome, with individual genes reflecting traits

of an individual. SLEUTH has five control

parameters, which vary from 0-100, termed

diffusion, breed, spread, slope and road growth. A

single run is controlled by the five values within the

integer range {0,0,0,0,0} to {100,100,100,100,100}.

The single set of five values forms a gene, and a

population of P such sets is the chromosome. Each

gene is evaluated, i.e. the model is run and the

fitness calculated. The genes are then sorted by

fitness, so that those that performed best rise to the

top. This is termed a generation. Between

generations, new genes are created by combining the

values of the best performing genes, after having

pairs of chromosomes “compete” to reproduce, and

so share their genes. Some of the genes in the

chromosome are mutated, by altering their values.

The mutation rate is the proportion of the

chromosome subjected to change. Mutation can be

by switching values or replacing values with random

numbers. There are two levels of fitness associated

with each generation: the total fitness of the

chromosome and the specific fitness of a gene. In

our case, we are interested in maximizing both total

fitness to move the training process forward, and the

fitness of the best performing gene, which is the best

model fit at that generation. Evolution ends when a

maximum number of generations is reached, or

when successive generations have no better total

fitness than their parents.

The chief variables in a GA include choosing the

size of the population (number of genes in the

chromosome), the maximum number of generations

(or minimum improvement in fitness to continue

evolution), the mutation rate, number of crossovers,

the number of offspring, and the number of

replacements. A second stopping criterion is the

maximum number of evaluations of genes for

possible inclusion as replacements. The GA

populates the initial chromosome with genes using

random numbers, standardizing values between zero

and one hundred. In one generation, each of the

genes is used as model input, and the fitness

criterion calculated. In Blecic et al.’s (2010) study,

the fitness values used were the Kappa coefficient

and the Lee-Sallee metric (Silva and Clarke 2005),

others have used the Optimal SLEUTH Metric

(Dietzel and Clarke 2007). This is repeated for all

genes in the chromosome, and the results ranked.

Each generation some proportion of the genes

are crossed over. For example a set of SLEUTH

input parameters may be {10, 20, 30, 40, 50}. After

mutation, it may be {10, 20, 50, 40, 30} with 2

values switched and 3 remaining. Another form of

mutation simply randomly or incrementally changes

one or more gene values. Lastly, the lowest

performing genes in terms of fitness are “killed off”

and replaced with new random values. Such a choice

increases the number of evaluations, when a

Improving SLEUTH Calibration with a Genetic Algorithm

321

maximum number is reached or a maximum number

of generations pass, the winning genome is selected.

This final replacement stage is important because

there is always a possibility that the chromosome

with the highest total fitness is not a global but only

a local maximum. Mutation and replacement ensure

that a superior value either evolves or arrives by

chance. The altered chromosome is then subjected to

the next generation, and the process is repeated

either until no further gain in fitness is achieved, or a

maximum number of generations exceeded.

While research continues on using GA as a

means to calibrate CA models, relatively few studies

have examined how the specifics of the GA impacts

the performance, accuracy and tractability of model

calibrations. Obviously this can only be answered in

the context of a single model. SLEUTH will be used

for this purpose because it is one of the few

instances where both brute force and GA calibration

options are available in open source code.

2.3 Calibrating SLEUTH

SLEUTH is a land use and land cover change model

based on two tightly coupled CA models: the Urban

Growth Model, that simulates how urban areas

expand and change; and the Deltatron model that

propagates urban changes into other land use types.

The model was originally developed and applied to

the San Francisco Bay area (Kirtland et al. 1994;

Clarke et al. 1997) and then to the Washington-

Baltimore area (Clarke et al. 1998). SLEUTH’s

initial calibration was by monolooping (trying all

possible settings for each parameter, holding the

others constant), but this was replaced by brute force

calibration (Clarke at al. 1996). The calibration

methods were systematically improved over decades

(Clarke et al. 2007; 2008a; 2008b; Chaudhuri and

Clarke 2013). Recently, research has examined the

goodness of fit between SLEUTH simulations and

actual data, usually using hindcasting and spatial

metrics of various kinds (Wu et al. 2009; Rienow

and Goetzke 2014; Sakieh 2013).

Noah Goldstein was the first to experiment

with GAs to calibrate SLEUTH (Goldstein 2004).

Others tried the same approach with more

sophisticated tools (Clarke-Lauer and Clarke 2011;

Jafarnezhad et al. 2015). Clarke-Lauer and Clarke

used the OSM as the fitness criterion and replaced

the brute force module in SLEUTH with a new code

routine that employed a GA that was posted to

SourceForge. Values that could be varied included

choices on encoding, fitness evaluation, crossover,

mutation and survival selection. Coding involved a

random number between 0 and 4 to index the five

SLEUTH control parameters (diffusion, breed,

spread, slope and road growth) and to decide how

many elements from the parent were to be

reproduced in the offspring. Remaining elements

were selected from the second parent, with the

second offspring using the opposite genes used for

the first. Parents were selected by tournament

selection, with a random set selected and the parents

chosen with the highest fitness. Each generation

replaces the weakest genes in the old population

with the strongest in the new. The SLEUTH-GA was

tested using the demo_city sample data set available

on the SLEUTH website. Mutation rates of 0.10 to

0.16 were found satisfactory, with a population size

of 25. The paper concluded that the GA produced a

speed up by a factor of 5 over brute force.

Jafarnezhad et al. (2015) used the SLEUTH-

GA code to apply SLEUTH to 3 cities in Golestan

Province, Iran. They calibrated SLEUTH first using

the standard brute force procedure, then used GA

with the fitness metric as the OSM. They coded their

own GA procedures based on Goldstein’s method

(Goldstein 2004). Model outputs were then

compared using the Receiving Operator Statistic

(ROC), landscape metrics and two Kappa

coefficients. Speed up over brute force was 4-5

times, and the authors noted that the results could be

improved by “testing different values for mutation

rate and decreasing model tendency to elitism.”

3 RESULTS

Existing SLEUTH data for San Diego, California

and Andijan, Uzbekistan were used (Syphard et al,

2011). The Andijan data set produced the lowest

OSM fits achieved by SLEUTH. In both cases these

were the best model calibrations, but they varied

substantially in predictive power. This is believed to

be because of Andijan’s extraordinary urban growth

history. The full set of results and data details are

published in Clarke (in press).

Both cities were then used with identical

inputs in the SLEUTH-GA version of the model

code. The SourceForge version was adjusted slightly

to take six parameters from the shell to be passed to

the code. These were the population size (genes in

the chromosome), the maximum number of

generations, the mutation rate, the maximum number

of evaluations per gene, the number of offspring,

and the maximum replacement number. Population

size, mutation rate, number of offspring, the

replacement number and the maximum number of

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322

evaluations were varied, while the other values were

held constant. The maximum number of generations

was set to 100, but in fact the GA rarely used more

than 20 generations in the calibration, contrary to the

higher numbers determined by Jafarnezhad et al.

(2015). The maximum number of evaluations for

substitution per chromosome was found to give peak

fitness at about 900, and this did not affect the

calibration process, other than increasing the number

of generations and CPU time.

Table 1: Brute Force Calibration Results. Values for

constants are after calibration, with high and low

coefficients in the top 8 solutions given, then after

averaging to the last time period.

San Diego Andijan

Calibration

period

1960-1999 1934-2013

Best OSM 0.7414836 0.0773797

Diffusion/derive

(100:98-100)

100

(63:60-63)

100

Breed/ derived (97:97-99)

100

(100:85-100)

100

Spread/ derived (25:24-25)

(1:1-2)

Slope/derived (15:15-18)

(80:75-79)

Road gravity/

derived

(53:45-53)

(25:15-25)

Calibration time

(s)

175589 440715

For Andijan the fitness was very low, with a

slight peak at a population size of 70. For San

Diego, the peak fitness occurred at a population size

of 55, so this value was then used for the next

monoloops. Similarly for mutation rates, the peak

fitness for both San Diego and Andijan was at a rate

of 0.13, so this value was used for all further

calibrations.

The information on calibration fine tuning for the

GA was rather limited from the Andijan case, so

testing of the ranges of the number of offspring and

the replacement number were restricted to the San

Diego data. Their best fitness values were 55 and 50

respectively. The final set of input parameters is

shown in Table 2. In particular, the maximum

number of evaluations sets the computation cost for

the run, and there appears to be a fine balance

between too many generations versus achieving a

good fit. A best value of 900 was selected, which

creates about 10-12 generations of evolution.

Table 2: Genetic Algorithm Parameter Monolooping

Calibration Results.

City San Diego Andijan

Calibration period 1960-1999 1934-2013

Best OSM 0.72972 0.07292

Maximum # of

evaluations

900 900

Population

(Chromosome size)

55 55

Mutation Rate 0.13 0.13

Number of offspring 55 55

Replacement per

generation

50 50

Calibration time (s) 55588 19866

This set of GA control parameters possibly

provides universal application for SLEUTH-GA

modeling. The values have been integrated into the

SLEUTH-GA code as defaults. This goes a long way

toward the fully automated and objective calibration

of SLEUTH, without user intervention (Straatman et

al 2004).

What range of parameters is there within the

chromosome that might still be improved by brute

force calibration over a smaller range, and what is

the impact of this difference on the actual forecasts

spatially? Table 3 shows the ranges of parameters in

the first gene subpopulation (highest performing

individuals of the 8 most fit parents) for the best GA

derived parameters. The maximum, average and

total fitness of a chromosome tend to peak

simultaneously, indicating that the best performing

chromosome is led by the most fit gene.

Table 3: Genetic Algorithm Calibration Results.

San Diego Andijan

Calibration

period

1960-1999 1934-2013

Best OSM 0.729724 0.072920

Diffusion/

derived

(90: 79-90)

100

(54:53-94)

Breed/ derived (23: 22-25)

(2:0-2)

Spread/ derived (89:74-98)

100

(88:62-94)

Slope/derived (13:2-32)

(70:9-70)

Road gravity/

derived

(19:19-98)

(47:14-47)

Calibration time

(s)

55588 19866

Speed Up 3.16 22.18

To investigate the spatial impact of the

differences in calibration mode, maps of forecast

Improving SLEUTH Calibration with a Genetic Algorithm

323

urbanization with a likelihood of over 50% were

created for the two cities and shown for both

methods of calibration (Figure 1). It is evident that

as in the calibrations, both cities are forecast with

higher uncertainty and greater spread using brute

force calibration, while the forecasts for both cities

are more constrained but with greater certainty using

GA. This appears to be the case both for high and

low model fit, and may be a robust way of providing

better forecasts.

Figure 1: Spatial extent of SLEUTH forecasts and Actual

Urban Growth During the Calibration Period.

4 CONCLUSION

Santé et al. (2010) pointed out the “need of making

urban CA more flexible while keeping their

simplicity by developing better calibration

methods.” This study has been in response to this

challenge. An important move, suggested by

Jafarnezhad et al. (2015) is to eliminate human

choices and judgements during the calibration

process, replacing the subjective with the objective

(Goldstein 2004). On the surface, replacing the brute

force calibration method for SLEUTH calibration

just substitutes a new set of calibration problems, i.e.

dealing with the characteristics of the gene and

determining how the evolutionary process yields the

best results. Prior work cited above, and now this

study, show that GA leads to at least equal, and

often superior calibration results while considerably

speeding the process. The results here also indicate

lower modeling uncertainty. The differences in the

calibration parameter sets are small, and the

differences among model forecasts are also small.

The advantages are the objectivity, and the benefits

of speed-up. At the least, GA can provide a

convergent set of genes that can be further optimized

by brute force over a much more limited parameter

set, such as the range over the top 8 genes listed in

table 3.

This study reviewed the importance of

calibration for CA land use change models.

Calibration performs important functions for models

because it ensures the model’s accuracy, integrity,

reliability and trustworthiness. Well calibrated

models are defensible and objective, and use real

world data instead of assumptions in their properties,

constants, variables and behavior types. There is an

obligation to perform sensitivity analyses and to run

controls. Moving SLEUTH calibration from brute

force to GA, the level of objectivity is further

improved. As a bonus, the amount of CPU time

devoted to calibration was reduced by about a factor

of 3 for San Diego and 22 for Andijan. Hopefully

this latter fact will enable new applications and new

cities to be simulated. The final version of the

SLEUTHGA software is posted at:

www.ncgia.ucsb.edu/projects/gig/Dnload/download.

htm and is available as open source code for

modelers.

REFERENCES

Blecic, I., Cecchini, A. and Trunfio, G. A. (2010) A

Comparison of Evolutionary Algorithms for

Automatic Calibration of Constrained Cellular

Automata. Taniar, D. et al. (Eds.): ICCSA 2010, LNCS

6016, 166–181. Springer-Verlag: Berlin.

Cao, K. Huang, B., Li, M. and Li, W. (2014) Calibrating a

cellular automata model for understanding rural–urban

land conversion: a Pareto front-based multiobjective

optimization approach, International Journal of

Geographical Information Science, 28:5, 1028-1046,

DOI: 10.1080/13658816.2013.851793

Cao, K., Wang, S., Li, X. and Chen, R. (2011) Modeling

Conversion of Rural-Urban Land Use Based on

Cellular Automata and Genetic Algorithm

Geoinformatics, 19th IEEE International Conference

on, Shanghai 1-5. DOI:

10.1109/GeoInformatics.2011.5981029

Clarke, K. C., Hoppen, S. and Gaydos, L. (1996) Methods

and Techniques for Rigorous Calibration of a Cellular

Automaton Model of Urban Growth. Proceedings, 3rd

Int. Conference/Workshop on Integrating GIS and

Environmental Modeling, January 21- 25th, Santa Fe,

NM.

Clarke, K. C., Hoppen, S. and L. Gaydos (1997) A self-

modifying cellular automaton model of historical

urbanization in the San Francisco Bay area.

Environment and Planning B: Planning and Design,

24, 247-261.

Clarke, K. C., and L. Gaydos (1998) Loose Coupling A

Cellular Automaton Model and GIS: Long-Term

GAMOLCS 2017 - International Workshop on Geomatic Approaches for Modelling Land Change Scenarios

324

Growth Prediction for San Francisco and

Washington/Baltimore. International Journal of

Geographical Information Science, 12, 7, 699-714.

Clarke, K.C. (2008a) Mapping and Modelling Land Use

Change: an Application of the SLEUTH Model, in

Landscape Analysis and Visualisation: Spatial Models

for Natural Resource Management and Planning,

(Eds. Pettit, C., Cartwright, W., Bishop, I., Lowell, K.,

Pullar, D. and Duncan, D.), Springer, Berlin, 353-366.

Clarke, K.C. (2008b) A Decade of Cellular Urban

Modeling with SLEUTH: Unresolved Issues and

Problems, Ch. 3 in Planning Support Systems for

Cities and Regions (Ed. Brail, R. K., Lincoln Institute

of Land Policy, Cambridge, MA, pp 47-60.

Clarke, K. C. (2014a) Why Simulate Cities? GeoJournal

79:129–136

Clarke, K. C. (2014b) Cellular Automata and Agent-Based

Models. Chapter 62 in Fischer, M. M. and Nijkamp, P.

(eds) Handbook of Regional Science. Springer-Verlag,

Berlin Heidelberg.

Clarke-Lauer, M. D., and Clarke, K. C. (2011). Evolving

simulation modeling: Calibrating SLEUTH using a

genetic algorithm. Proc., 11th Int. Conf. on Geo

Computation, Univ. College London, London.

Clarke, K. C. (in press) Land Use Change Modeling with

SLEUTH: Improving Calibration with a Genetic

Algorithm. In MT Camacho Olmedo, M Paegelow, JF

Mas, F Escobar (eds.) Geomatic approaches for

modelling land change scenarios . Lecture Notes in

Geoinformation and Cartography LNGC series.

Springer Verlag.

Chaudhuri, G. and Clarke, K. C. (2013) The SLEUTH

Land Use Change Model: A Review. Int. Journal of

Environmental Resources Research, 1, 1, 88-104.

Colonna, A., Di Stefano, V., Lombardo, S., Papini, L.,

Rabino, G. A. (1998). Learning urban cellular

automata in a real world: The case study of Rome

metropolitan area. In: ACRI’98 third conference on

cellular automata for research and industry, Trieste,

7–9 October 1998. London: Springer, 165–18.

Dietzel, C. and Clarke, K. C. (2007) Toward Optimal

Calibration of the SLEUTH Land Use Change Model.

Transactions in GIS, 11, 1, 29-45.

Feng, Y., Liu, Y., Tong, X., Liu, M., and Deng, S. (2011).

Modeling dynamic urban growth using cellular

automata and particle swarm optimization rules.

Landscape and Urban Planning, 102(3), 188–196.

http://dx.doi.org/10.1016/j.landurbplan.2011.04.004.

Feng, Y. and Liu, Y., 2012. An optimised cellular

automata model based on adaptive genetic algorithm

for urban growth simulation. In: W. Shi, A. Yeh, Y.

Leung and C. Zhou, eds. Advances in spatial data

handling and GIS: 14th international symposium on

spatial data handling. Heidelberg, Germany: Springer,

27–38.

García , A. M. I. Santé , M. Boullón and R. Crecente

(2013) Calibration of an urban cellular automaton

model by using statistical techniques and a genetic

algorithm. Application to a small urban settlement of

NW Spain, International Journal of Geographical

Information Science, 27:8, 1593-1611, DOI:

10.1080/13658816.2012.762454

Goldstein, N.C. (2004). Brains vs. Brawn: Comparative

strategies for the calibration of a cellular automata-

based urban growth model. In: P. Atkinson, G. Foody,

S. Darby and F. Wu, eds., GeoDynamics. Boca Raton,

FL: CRC Press.

Gong, Z., Tang, W., and Thill, J. C. (2012).

Parallelization of ensemble neural networks for spatial

land-use modeling. In Proceedings of the 5th ACM

SIGSPATIAL international workshop on location-

based social networks (pp. 48–54). ACM.

Guan, Q., Wang, L. and Clarke, K. C. (2005) An

Artificial-Neural-Network-based, Constrained CA

Model for Simulating Urban Growth . Cartography

and Geographic Information Science. 32, 4, 369-380.

Houet, T., Aguejdad, R., Doukari, O., Battaia G. and

Clarke, K. (2016) “Description and validation of a

‘non path-dependent’ model for projecting contrasting

urban growth futures”, Cybergeo : European Journal

of Geography, Systèmes, Modélisation,

Géostatistiques, document 759

http://cybergeo.revues.org/27397

Holland J. H. (1998). Emergence: From Chaos to Order.

Addison-Wesley, Redwood City, CA.

Hu, Z., and Lo, C. (2007). Modeling urban growth in

Atlanta using logistic regression. Computers,

Environment and Urban Systems, 31(6), 667–688.

Jafarnezhad, J., Salmanmahiny, A., and Sakieh, Y. (2015).

Subjectivity versus objectivity—Comparative study

between Brute Force method and Genetic Algorithm

for calibrating the SLEUTH urban growth model.

Urban Planning and Development.

doi:10.1061/(ASCE)UP.1943-5444.0000307.

Kirtland, D. Gaydos, L. Clarke, K. C., DeCola, L.,

Acevedo, W. and Bell, C. (1994) An analysis of

human-induced land transformations in the San

Francisco Bay/Sacramento area. World Resources

Review, 6, 2, 206-217.

Li, X. and Gar-On Yeh, A. (2002) Neural-network-based

cellular automata for simulating multiple land use

changes using GIS. International Journal of

Geographic Information Science. 16, 4, 323-343.

Li, X., and Yeh, A. G. O. (2004). Data mining of cellular

automata’s transition rules. International Journal of

Geographical Information Science, 18, 723–744.

Liu, Y., and Phinn, S. R. (2003). Modelling urban

development with cellular automata incorporating

fuzzy-set approaches. Computers, Environment, and

Urban Systems, 27, 637–658.

Long, Y., Mao, Q., and Dang, A. (2009). Beijing urban

development model: Urban growth analysis and

simulation. Tsinghua Science and Technology, 14(6),

782–794.

National Research Council (2014) Advancing Land

Change Modeling: Opportunities and Research

Requirements. Geographical Sciences Committee:

Washington D. C.; National Academy Press.

Pijanowski, B.C., B. Shellito and S. Pithadia. 2002. Using

artificial neural networks, geographic information

Improving SLEUTH Calibration with a Genetic Algorithm

325

systems and remote sensing to model urban sprawl in

coastal watersheds along eastern Lake Michigan.

Lakes and Reservoirs 7: 271-285.

Pontius Jr, R. G., W. Boersma, J-C. Castella, K. Clarke, T.

de Nijs, C. Dietzel, D. Zengqiang, E. Fotsing, N.

Goldstein, K. Kok, E. Koomen, C. D. Lippitt, W.

McConnell, A. M. Sood, B. Pijanowski, S. Pithadia, S.

Sweeney, T. N. Trung, A. T. Veldkamp, and P. H.

Verburg. (2007) Comparing the input, output, and

validation maps for several models of land change.

Annals of Regional Science, 42, 1 , 11-37.

Rienow, A. and Goetzke, R. (2014) “Supporting

SLEUTH—Enhancing a cellular with support vector

machines for urban growth modeling. Computers,

Environment and Urban Systems. 49, 66-81.

Sakieh, Y. (2013) Urban sustainability analysis through

the SLEUTH urban growth model and multi-criterion

evaluation. A case study of Karaj City. PhD

Dissertation, University of Tehran, Iran.

Santé, I., Garcia, A. M., Miranda, D. and Maseda, R. C.

(2010). Cellular automata models for the simulation of

real-world urban processes: a review and analysis.

Landscape and Urban Planning, 96 (2), 108–122.

Shan, J., Alkheder, S., and Wang, J., 2008. Genetic

algorithms for the calibration of cellular automata

urban growth modeling. Photogrammetric

Engineering and Remote Sensing, 74 (10), 1267–1277.

Silva, E.A. and Clarke, K.C. (2002) Calibration of the

SLEUTH urban growth model for Lisbon and Porto,

Portugal Computers, Environment and Urban Systems,

26, 6, 525-52 DOI 10.1016/S0198-9715(01)00014-X

Silva, E. A., and Clarke, K. C. (2005). “Complexity,

emergence and cellular urban models: Lessons learned

from applying SLEUTH to two Portuguese

metropolitan areas.” European Planning Studies,

13(1), 93–115.

Syphard, A. D., Clarke, K. C. Franklin, J., Regan, H. M.

and Mcginnis, M. (2011) Forecasts of habitat loss and

fragmentation due to urban growth are sensitive to

source of input data, Journal of Environmental

Management, 92, 7, 1882-1893.

Straatman, B., White, R., and Engelen, G., 2004. Towards

an automatic calibration procedure for constrained

cellular automata. Computers, Environment and

Urban Systems, 28, 149–170.

Torrens, P. T. and O’Sullivan, D (2001) Cellular automata

and urban simulation: where do we go from here?

Environment and Planning B: Planning and Design

28(2):163–168

Wu, F. and Webster, C. J. (1998) Simulation of land

development through the integration of cellular

automata and multi-criteria evaluation. Environment

and Planning B: Planning and Design 25(1):103–126

Wu, F. (2002) Calibration of stochastic cellular automata:

the application to rural-urban land conversions,

International Journal of Geographical Information

Science. 16 (8), 795–818.

Wu, X., Y. Hu, H. He, R. Bu, J. Onsted, and F. Xi (2009)

Performance Evaluation of the SLEUTH Model in the

Shenyang Metropolitan Area of Northeastern China

Environmental Modeling and Assessment 14, 2, 221-

230.

Xiang, W-N, and K. C. Clarke (2003) The use of scenarios

in land use planning. Environment and Planning B:

Planning and Design, 30, 885-909.

Yang, Q. S. and X. Li, (2007) Calibrating urban cellular

automata using genetic algorithms, Geographical

Research. 26, 2, 229–237.

Yang, Q., Li, X., and Shi, X. (2008). Cellular automata for

simulating land use changes based on support vector

machines. Computers and Geosciences, 34(6), 592–

602.

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