Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale

2014 ◽  
Vol 53 ◽  
pp. 160-171 ◽  
Author(s):  
Reine Maria Basse ◽  
Hichem Omrani ◽  
Omar Charif ◽  
Philippe Gerber ◽  
Katalin Bódis
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Artificial Neural Networks ◽  
Cellular Automata ◽  
Land Use Changes ◽  
Explicit Representation ◽  
Border Region ◽  
Cross Border ◽  
Land Cover Dynamics ◽  
Region Scale

scholarly journals DEVELOPING AN AUTOMATED METHOD FOR THE APPLICATION OF LIDAR IN IUMAT LAND-USE MODEL: ANALYSIS OF LAND-USE CHANGES USING BUILDING-FORM PARAMETERIZATION, GIS, AND ARTIFICIAL NEURAL NETWORKS

2019 ◽  
Vol 14 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Mohamad Farzinmoghadam ◽  
Nariman Mostafavi ◽  
Elisabeth Hamin Infield ◽  
Simi Hoque
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Artificial Neural Networks ◽  
Land Use Change ◽  
Land Use Changes ◽  
Growth Patterns ◽  
Urban Metabolism ◽  
Automated Method ◽  
Land Use Model ◽  
Building Form

Predicting resource consumption in the built environment and its associated environmental consequences is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. The study of land-use change has many implications for infrastructure design, resource allocation, and urban metabolism simulation. While most urban models focus on horizontal growth patterns, few investigate the impacts of vertical characteristics of urbanscapes in predicting land-use changes. In this paper, Building-form variables are introduced as a new determinant factor for investigating effects of vertical characteristics of an urbanscape in predicting land-use change. This work outlines an automated method for generating building-form variables from Light Detection and Ranging (LIDAR) data by using Density-Based Spatial Clustering and normal equations. This paper presents a Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict urban growth patterns within the IUMAT framework (Integrated Urban Metabolism Analysis Tool), which is an analytical platform for quantifying the overall sustainability in the urbanscape. The town of Amherst in Western Massachusetts (for the period of 1971–2005) is used as a case study for testing the model. By isolating the weights of each explanatory variable in models, this study highlights the influence of building geometry on future development scenarios.

Simulating Urban Dynamics Using Cellular Automata

2011 ◽  
pp. 125-139 ◽  
Author(s):  
Xia Li
Keyword(s):  
Neural Networks ◽  
Land Use ◽  
Cellular Automata ◽  
Land Use Changes ◽  
Dynamics Simulation ◽  
Spatial Variables ◽  
Urban Dynamics ◽  
Multiple Land Use ◽  
Land Use Dynamics

This chapter introduces the concepts of cellular automata (CA) which have been increasingly used for simulating urban dynamics. Simulation and prediction of urban evolution can provide the useful inputs to crime models. However, calibration of urban cellular automata is crucial for simulating realistic cities. Simulation of multiple land use changes using CA is difficult because numerous spatial variables and parameters have to be utilized. The incorporation of neural networks with CA can alleviate the calibration problems. This chapter illustrates how complex land use dynamics can be simulated by the integration of CA and neural networks.

scholarly journals Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework

2019 ◽  
Vol 8 (10) ◽  
pp. 454 ◽  
Author(s):  
Junfeng Kang ◽  
Lei Fang ◽  
Shuang Li ◽  
Xiangrong Wang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Cellular Automata ◽  
Markov Model ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Remote Sensing Images ◽  
Mapreduce Framework ◽  
Construction Land ◽  
Model Based

The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.

scholarly journals Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

2020 ◽  
Vol 12 (4) ◽  
pp. 1396
Author(s):  
Shufang Wang ◽  
Xiyun Jiao ◽  
Liping Wang ◽  
Aimin Gong ◽  
Honghui Sang ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Markov Model ◽  
Land Use Changes ◽  
Regression Trees ◽  
Boosted Regression Trees ◽  
Utilization Rate ◽  
Protection Measures ◽  
Unused Land ◽  
The Government

The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

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