Calibrating a cellular automata model for understanding rural-urban land conversion: a Pareto front-based multi-objective optimization approach

Cellular automata (CA) modeling is useful to assist in understanding rural–urban land conversion processes. Although CA calibration is essential to ensuring an accurate modeling outcome, it remains a significant challenge. This study aims to address that challenge by developing and evaluating a mult...

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Main Authors: CAO, Kai, HUANG, Bo, LI, Manchun, LI, Wenwen
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/5405
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6408&context=sis_research
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spelling sg-smu-ink.sis_research-64082020-12-11T06:33:57Z Calibrating a cellular automata model for understanding rural-urban land conversion: a Pareto front-based multi-objective optimization approach CAO, Kai HUANG, Bo LI, Manchun LI, Wenwen Cellular automata (CA) modeling is useful to assist in understanding rural–urban land conversion processes. Although CA calibration is essential to ensuring an accurate modeling outcome, it remains a significant challenge. This study aims to address that challenge by developing and evaluating a multi-objective optimization model that considers the objectives of minimizing minus maximum likelihood estimation (MLE) value and minimizing number of errors (NOE) when calibrating CA transition rules. A Pareto front-based heuristic search algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is used to obtain optimal or near-optimal solutions. The proposed calibration approach is validated using a case study from New Castle County, Delaware, United States. A comparison of the NSGA-II-based calibration model, the generic Logit regression calibration approach (MLE-based Generic Genetic Algorithm (GGA) calibration approach), and the NOE-based GGA calibration approach demonstrates that the proposed calibration model can produce stable solutions with better simulation accuracy. Furthermore, it can generate a set of solutions with different preferences regarding the two objectives which can provide CA simulation with robust parameters options. 2014-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5405 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6408&context=sis_research http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University NSGA-II land conversion rural–urban cellular automata calibration Logit regression Databases and Information Systems Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic NSGA-II
land conversion
rural–urban
cellular automata
calibration
Logit regression
Databases and Information Systems
Theory and Algorithms
spellingShingle NSGA-II
land conversion
rural–urban
cellular automata
calibration
Logit regression
Databases and Information Systems
Theory and Algorithms
CAO, Kai
HUANG, Bo
LI, Manchun
LI, Wenwen
Calibrating a cellular automata model for understanding rural-urban land conversion: a Pareto front-based multi-objective optimization approach
description Cellular automata (CA) modeling is useful to assist in understanding rural–urban land conversion processes. Although CA calibration is essential to ensuring an accurate modeling outcome, it remains a significant challenge. This study aims to address that challenge by developing and evaluating a multi-objective optimization model that considers the objectives of minimizing minus maximum likelihood estimation (MLE) value and minimizing number of errors (NOE) when calibrating CA transition rules. A Pareto front-based heuristic search algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is used to obtain optimal or near-optimal solutions. The proposed calibration approach is validated using a case study from New Castle County, Delaware, United States. A comparison of the NSGA-II-based calibration model, the generic Logit regression calibration approach (MLE-based Generic Genetic Algorithm (GGA) calibration approach), and the NOE-based GGA calibration approach demonstrates that the proposed calibration model can produce stable solutions with better simulation accuracy. Furthermore, it can generate a set of solutions with different preferences regarding the two objectives which can provide CA simulation with robust parameters options.
format text
author CAO, Kai
HUANG, Bo
LI, Manchun
LI, Wenwen
author_facet CAO, Kai
HUANG, Bo
LI, Manchun
LI, Wenwen
author_sort CAO, Kai
title Calibrating a cellular automata model for understanding rural-urban land conversion: a Pareto front-based multi-objective optimization approach
title_short Calibrating a cellular automata model for understanding rural-urban land conversion: a Pareto front-based multi-objective optimization approach
title_full Calibrating a cellular automata model for understanding rural-urban land conversion: a Pareto front-based multi-objective optimization approach
title_fullStr Calibrating a cellular automata model for understanding rural-urban land conversion: a Pareto front-based multi-objective optimization approach
title_full_unstemmed Calibrating a cellular automata model for understanding rural-urban land conversion: a Pareto front-based multi-objective optimization approach
title_sort calibrating a cellular automata model for understanding rural-urban land conversion: a pareto front-based multi-objective optimization approach
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/5405
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6408&context=sis_research
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