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Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms Amin Tayyebi Purdue University

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Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms Amin Tayyebi Purdue University Purdue University Purdue e-Pubs Open Access Dissertations Theses and Dissertations 2013 Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms Amin Tayyebi Purdue University Follow this and additional works at: https://docs.lib.purdue.edu/open_access_dissertations Part of the Civil Engineering Commons, Computer Sciences Commons, and the Urban Studies and Planning Commons Recommended Citation Tayyebi, Amin, "Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms" (2013). Open Access Dissertations. 1. https://docs.lib.purdue.edu/open_access_dissertations/1 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. i SIMULATING LAND USE LAND COVER CHANGE USING DATA MINING AND MACHINE LEARNING ALGORITHMS A Dissertation Submitted to the Faculty of Purdue University by Amin Tayyebi In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2013 Purdue University West Lafayette, Indiana ii “The price of success is hard work, dedication to the job at hand, and the determination that whether we win or lose, we have applied the best of ourselves to the task at hand” Vince Lombardi “I really owe everything to my parents and their devotion and drive to see to it that their children had the education which led to the opportunities that they never were able to have” George J. Mitchell iii ACKNOWLEDGMENTS I would like to say warm thanks to Prof. Bryan C. Pijanowski who helped me during last 4 years as a co-chair of my master program and as a chair of my PhD program. His flexibility, patience and remarkable contribution for high quality work were encouraging for me to complete my PhD promptly. I am also very thankful to the other members of my dissertation committee: Prof. Jeffrey Holland, Prof. Guofan Shao and Prof. Songlin Fei, for their academic support. I would also like to thank the United States Geological Survey (USGS) and National Science Foundation (NSF) which provided funds to support a research assistantship that supported the two years of my PhD. I would also like to say thanks to each of my lab-mates, Dr. Burak Pekin, Jarrod Doucette, James Plourde, Sarah Dumyahn, Luis Villanueva, Kim Robinson, NahNah Kim and Maryam Ghadiri, for the encouragement they provided. My special gratitude is also extended to Kelly Garret who has answered to every single question I asked her about the Graduate School. It is an honor for me to be a contributing member of the land use land cover change (LUCC) community. I would like to give special thanks to the LUCC scientists who gave me encouragement (Prof. Micheal Batty; Prof. Bryan C. Pijanowski; Prof. Keith Clarke; Prof. Robert G. Pontius; Prof. Peter Verburg; Prof Tom Veldkamp; Prof. iv Xia Li; Prof. Anthony Gar-On Yeh; Prof. John D. Landis; Prof. Danielle J. Marceau; Prof. Jie Shan; Prof. Peter Deadman; Prof. Zhiyong Hu) and other scientists (Prof. Billie L. Turner; Prof. Eric Lambin; Prof. Dan Brown, Prof. Erle Ellis; Dr. Thomas Albright; Prof. Thomas Saaty; Prof. Kevin Gurney; Dr. Robert Kennedy; Prof. Helen Briassoulis; Prof. Eric Koomen) who assess the impact of LUCC on other fields (e.g. climate, biodiversity, hydrology, pollution and so on). I asked each of them to make a video approximately 5 minutes in length where they discuss LUCC modeling and what they have contributing to our understanding of the impacts of land use change on the environment. So, I would like give a special “thanks” to those that contributed to the online video archive. v TABLE OF CONTENTS Page LIST OF TABLES ............................................................................................................. ix LIST OF FIGURES ........................................................................................................... xi ABBREVIATIONS ......................................................................................................... xiv ABSTRACT ..................................................................................................................... xvi CHAPTER 1: LAND CHANGE SCIENCE: A BRIEF INTRODUCTION ...................... 1 1.1 Introduction ................................................................................................... 1 1.2 Research objectives and structure of the dissertation ................................... 4 1.3 References ..................................................................................................... 7 CHAPTER 2: LAND CHANGE SCIENCE THROUGH THE LENS OF THE LAND TRANSFORMATION MODEL (LTM) ............................................................................ 9 2.1 Introduction ................................................................................................... 9 2.2 LTM as a case-study LUCC Model ............................................................ 10 2.3 LTM used in land-climate interaction studies ............................................ 14 2.4 LTM in land-hydrologic dynamic studies ................................................... 15 2.5 Organismal responses to LUCC.................................................................. 18 2.6 Joint LUCC and climate change effect on biodiversity .............................. 19 2.7 LTM and planning decisions ...................................................................... 20 2.8 Quantifying error propagation in coupled models containing the LTM ..... 21 2.9 Conclusion .................................................................................................. 23 2.10 References ................................................................................................. 24 CHAPTER 3: A SUMMARY OF LAND USE LAND COVER CHANGE MODELS .. 31 3.1 Introduction ................................................................................................. 31 3.2 Calibration, validation and null models ...................................................... 32 3.3 Cellular automata models ........................................................................... 34 3.3.1 Cellular automata overview ................................................................. 34 3.3.2 Slope, land use, exclusion, urban, transportation, and hill shading (SLEUTH)................................................................................................................. 35 3.3.3 Geo-simulation ..................................................................................... 37 3.3.4 Vector based CA (VEC-GCA) ............................................................. 38 3.4 Weighted-map models ................................................................................ 39 3.4.1 Multi criteria evaluation (MCE) .......................................................... 39 3.4.2 Geomod ................................................................................................ 40 3.5 Regression based models ............................................................................ 42 3.5.1 Logistic regression overview ............................................................... 42 3.5.2 Conversion of land use and its effects (CLUE) ................................... 43 3.6 Agent based models (ABMs) ...................................................................... 44 3.7 Machine learning ........................................................................................ 45 vi Page 3.7.1 Artificial neural networks (ANNs) ...................................................... 45 3.7.2 Support vector machines (SVMs) ........................................................ 46 3.7.3 Genetic algorithms (GAs) .................................................................... 47 3.8 Data mining ................................................................................................. 47 3.8.1 Classification and regression tree (CART) .......................................... 48 3.8.2 Multiple adaptive regression splines (MARS) ..................................... 48 3.9 Hybrid models of CA .................................................................................. 49 3.9.1 CA-MCE .............................................................................................. 49 3.9.2 CA-SVM .............................................................................................. 50 3.9.3 CA-GA ................................................................................................. 50 3.9.4 CA-ANN .............................................................................................. 51 3.10 Urban growth boundary models (UGBMs) .............................................. 52 3.10.1 ANN UGBM ...................................................................................... 52 3.11 Rule based UGBMs .................................................................................. 52 3.11.1 Distance dependent model (DDM) .................................................... 53 3.11.2 Distance independent model (DIM) ................................................... 53 3.12 Conclusion ................................................................................................ 54 3.13 References ................................................................................................. 64 CHAPTER 4: USING CART, MARS AND ANNS TO MODEL LAND USE LAND COVER CHANGE: APPLICATION OF DATA MINING TOOLS TO THREE DIVERSE AREAS IN THE USA AND AFRICA UNDERGOING LAND TRANSFORMATION .....................................................................................................
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