Simulation of land use changes using cellular automata and artificial neural network

This paper presents a method integrating artificial neural network (ANN) in cellular automata (CA) to simulate land use changes in Luxembourg and the areas adjacent to its borders. The ANN is used as a base of CA model transition rule. The proposed method shows promising results for prediction of land use over time. The ANN is validated using cross-validation technique and Receiver Operating Characteristic (ROC) curve analysis, and compared with logit model and a support vector machine approach. The application described in this paper highlights the interest of integrating ANNs in CA based model for land use dynamic simulation.Artificial neural network; Cellular automata; Modelling; Land use changes; Spatial planning and dynamics

Integrated urban evolutionary modeling

Cellular automata models have proved rather popular as frameworks for simulating the physical growth of cities. Yet their brief history has been marked by a lack of application to real policy contexts, notwithstanding their obvious relevance to topical problems such as urban sprawl. Traditional urban models which emphasize transportation and demography continue to prevail despite their limitations in simulating realistic urban dynamics. To make progress, it is necessary to link CA models to these more traditional forms, focusing on the explicit simulation of the socio-economic attributes of land use activities as well as spatial interaction. There are several ways of tackling this but all are based on integration using various forms of strong and loose coupling which enable generically different models to be connected. Such integration covers many different features of urban simulation from data and software integration to internet operation, from interposing demand with the supply of urban land to enabling growth, location, and distributive mechanisms within such models to be reconciled. Here we will focus on developin

Linnade laienemine Eestis: seire, analüüs ja modelleerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneLinnade laienemine, mida iseloomustab vähese tihedusega, ruumiliselt ebaühtlane ja hajutatud areng linna piiridest välja. Kuna linnade laienemine muudab põllumajandus- ja metsamaid ning väikesed muutused linnapiirkondades võivad pikaajaliselt mõjutada elurikkust ja maastikku, on hädavajalik seirata linnade ruumilist laienemist ning modelleerida tulevikku, saamaks ülevaadet suundumustest ja tagajärgedest pikemas perspektiivis. Eestis võeti pärast taasiseseisvumist 1991. aastal vastu maareformi seadus ning algas “maa” üleandmine riigilt eraomandisse. Sellest ajast peale on Eestis toimunud elamupiirkondade detsentraliseerimine, mis on mõjutanud Tallinna ümbruse põllumajandus- ja tööstuspiirkondade muutumist, inimeste elustiili muutusi ning jõukate inimeste elama asumist ühepereelamutesse Tallinna, Tartu ja Pärnu lähiümbruse. Selle aja jooksul on Eesti rahvaarv vähenenud 15,31%. Käesoleva doktoritöö eesmärgiks on "jälgida, analüüsida ja modelleerida Eesti linnade laienemist viimase 30 aasta jooksul ning modelleerida selle tulevikku", kasutades paljusid modelleerimismeetodeid, sealhulgas logistilist regressiooni, mitmekihilisi pertseptronnärvivõrke, rakkautomaate, Markovi ahelate analüüsi, mitme kriteeriumi. hindamist ja analüütilise hierarhia protsesse. Töö põhineb neljal originaalartiklil, milles uuriti linnade laienemist Eestis. Tegu on esimese põhjaliku uuringuga Eesti linnade laienemise modelleerimisel, kasutades erinevaid kaugseireandmeid, mõjutegureid, parameetreid ning modelleerimismeetodeid. Kokkuvõtteks võib öelda, et uusehitiste hajumismustrid laienevad jätkuvalt suuremate linnade ja olemasolevate elamupiirkondade läheduses ning põhimaanteede ümber.Urban expansion is characterized by the low–density, spatially discontinued, and scattered development of urban-related constructions beyond the city boundaries. Since urban expansion changes the agricultural and forest lands, and slight changes in urban areas can affect biodiversity and landscape on a regional scale in the long-term, spatiotemporal monitoring of urban expansion and modeling of the future are essential to provide insights into the long-term trends and consequences. In Estonia, after the regaining independence in 1991, the Land Reform Act was passed, and the transfer of “land” from the state to private ownership began. Since then, Estonia has experienced the decentralization of residential areas affecting the transformation of agricultural and industrial regions around Tallinn, changes in people's lifestyles, and the settling of wealthy people in single-family houses in the suburbs of Tallinn, Tartu, and Pärnu. During this period, Estonia's population has declined dramatically by 15.31%. Therefore, this dissertation aims to "monitor, analyze and model Estonian urban expansion over the last 30 years and simulate its future" using many modeling approaches including logistic regression, multi-layer perceptron neural networks, cellular automata, Markov chain Analysis, multi-criteria evaluation, and analytic hierarchy process. The thesis comprises four original research articles that studied urban expansion in Estonia. So far, this is the first comprehensive study of modeling Estonian urban expansion utilizing various sets of remotely sensed data, driving forces and predictors, and modeling approaches. The scattering patterns of new constructions are expected to continue as the infilling form, proximate to main cities and existing residential areas and taking advantage of main roads in future.https://www.ester.ee/record=b550782

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Modelling urban spatial change: a review of international and South African modelling initiatives

August 2013Urban growth and land use change models have the potential to become important tools for urban spatial planning and management. Before embarking on any modelling, however, GCRO felt it was important to take note of, and critically assess lessons to be learnt from international experience and scholarship on spatial modelling, as well as a number of South African experiments that model future urban development. In 2012, GCRO initiated preliminary research into current international and South African modelling trends through a desktop study and telephone, email and personal interviews. This Occasional paper sets out to investigate what urban spatial change modelling research is currently being undertaken internationally and within South Africa. At the international level, urban modelling research since 2000 is reviewed according to five main categories: land use transportation (LUT), cellular automata, urban system dynamics, agent-based models (ABMs) and spatial economics/econometric models (SE/EMs). Within South Africa, urban modelling initiatives are categorised differently and include a broader range of urban modelling techniques. Typologies used include: provincial government modelling initiatives in Gauteng; municipal government modelling initiatives; other government-funded modelling research; and academic modelling research. The various modelling initiatives described are by no means a comprehensive review of all urban spatial change modelling projects in South Africa, but provide a broad indication of the types of urban spatial change modelling underway. Importantly, the models may form the basis for more accurate and sophisticated urban modelling projects in the future. The paper concludes by identifying key urban modelling opportunities and challenges for short- to long-term planning in the GCR and South Africa.Written by Chris Wray, Josephine Musango and Kavesha Damon (GCRO) Koech Cheruiyot (NRF:SARChI chair in Development Planning and Modelling at Wits

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

Cellular automata (CA) is a powerful tool for modeling the evolution of macroscopic scale phenomena as it couples time, space, and variables together while remaining in a simplified form. However, such application has remained challenging in forest insect epidemics due to the highly dynamic nature of insect behavior. Recent advances in temporal trajectory-based image analysis offer an alternative way to obtain high-frequency model calibration data. In this study, we propose an insect-CA modeling framework that integrates cellular automata, remote sensing, and Geographic Information System to understand the insect ecological processes, and tested it with measured data of mountain pine beetle (MPB) in the Rocky Mountains. The overall accuracy of the predicted MPB mortality pattern in the test years ranged from 88% to 94%, which illuminates its effectiveness in modeling forest insect dynamics. We further conducted sensitivity analysis to examine responses of model performance to various parameter settings. In our case, the ensemble random forest algorithm outperforms the traditional linear regression in constructing the suitability surface. Small neighborhood size is more effective in simulating the MPB movement behavior, indicating that short-distance is the dominating dispersal mode of MPB. The introduction of a stochastic perturbation component did not improve the model performance after testing a broad range of randomness degree, reflecting a relative compact dispersal pattern rather than isolated outbreaks. We conclude that CA with remote sensing observation is useful for landscape insect movement analyses;however, consideration of several key parameters is critical in the modeling process and should be more thoroughly investigated in future work

Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

The integrated - environmental, economic and social - analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

Using scenario modelling for adapting to urbanization and water scarcity: towards a sustainable city in semi-arid areas

Sustainable development on a global scale has been hindered by urbanization and water scarcity, but the greatest threat is from decision-makers ignoring these challenges, particularly in developing countries. In addition, urbanization is spreading at an alarming rate across the globe, affecting the environment and society in profound ways. This study reviews previous studies that examined future scenarios of urban areas under the challenges of rapid population growth, urban sprawl and water scarcity, in order to improve supported decision-making (SDM). Scholars expected that the rapid development of the urbanization scenario would cause resource sustainability to continually be threatened as a result of excessive use of natural resources. In contrast, a sustainable development scenario is an ambitious plan that relies on optimal land use, which views land as a limited and non-renewable resource. In consequence, estimating these threats together could be crucial for planning sustainable strategies for the long term. In light of this review, the SDM tool could be improved by combining the cellular automata model, water evolution and planning model coupled with geographic information systems, remote sensing and criteria analytic hierarchical process modelling. Urban planners could optimize, simulate and visualize the dynamic processes of land-use change and urban water, using them to overcome critical conditions