Title
Predicting land use change with data-driven models
Creator
Samardžić-Petrović, Mileva, 1980-
Copyright date
2014
Object Links
Select license
Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
License description
Dozvoljavate samo preuzimanje i distribuciju dela, ako/dok se pravilno naznačava ime autora, bez ikakvih promena dela i bez prava komercijalnog korišćenja dela. Ova licenca je najstroža CC licenca. Osnovni opis Licence: http://creativecommons.org/licenses/by-nc-nd/3.0/rs/deed.sr_LATN. Sadržaj ugovora u celini: http://creativecommons.org/licenses/by-nc-nd/3.0/rs/legalcode.sr-Latn
Language
English
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 01.10.2014.
Other responsibilities
mentor
Bajat, Branislav, 1963-
mentor
Kovačević, Miloš, 1968-
član komisije
Cvijetinović, Željko, 1965-
član komisije
Dragićević, Suzana.
član komisije
Đorđević, Dejan, 1966-
Academic Expertise
Tehničko-tehnološke nauke
University
Univerzitet u Beogradu
Faculty
Građevinski fakultet
Alternative title
Predviđanje promena u korišćenju zemljišta primenom modela vođenih podacima (DATA-DRIVEN MODELS)
Publisher
[M. Samardžić-Petrović]
Format
XVI, 142 lista
description
Geodesy - Land Information System / Геодезија - Земљишни информациони системи
Abstract (en)
One of the main tasks of data-driven modelling methods is to induce a
representative model of underlying spatial - temporal processes using past data
and data mining and machine learning approach. As relatively new methods,
known to be capable of solving complex nonlinear problems, data-driven methods
are insufficiently researched in the field of land use. The main objective of this
dissertation is to develop a methodology for predictive urban land use change
models using data-driven approach together with evaluation of the performance of
different data-driven methods, which in the stage of finding patterns of land use
changes use three different machine learning techniques: Decision Trees, Neural
Networks and Support Vector Machines. The proposed methodology of data-driven
methods was presented and special attention was paid to different data
representation, data sampling and the selection of attributes by four methods (χ2,
Info Gain, Gain Ratio and Correlation-based Feature Subset) that best describe the
process of land use change. Additionally, a sensitivity analysis of the Support
Vector Machines -based models was performed with regards to attribute selection
and parameter changes. Development and evaluation of the methodology was
performed using data on three Belgrade municipalities (Zemun, New Belgrade and
Surčin), which are represented as 10×10 m grid cells in four different moments in
time (2001, 2003, 2007 and 2010).
The obtained results indicate that the proposed data-driven methodology provides
predictive models which could be successfully used for creation of possible
scenarios of urban land use changes in the future. All three examined machine
learning techniques are suitable for modeling land use change. Accuracy and
performance of models can be improved using proposed balanced data sampling,
including the information about neighbourhood and history in data
representations and relevant attribute selections. Additionally, using selected
subset of attributes resulted in a simple model and with less possibility to be
overfitted with higher values of Support Vector Machines parameters.
Abstract (sr)
Један од главних задатака моделирања метода вођених подацима (Data-driven methods) је проналажење репрезентативног модела испитивног просторно временског процеса, применом података из прошлости и Data Mining и Machine Learning приступа...
Authors Key words
data-driven modeling, data mining, machine learning, spatial-temporal
modeling, land use changes, Geographic Information Systems
Authors Key words
модели вођени подацима (data-driven methods), машинско учење, просторно-временско моделирање, промена коришћења земљишта, географски информациони системи
Classification
007:528.9]:004(043.3)
Type
Tekst
Abstract (en)
One of the main tasks of data-driven modelling methods is to induce a
representative model of underlying spatial - temporal processes using past data
and data mining and machine learning approach. As relatively new methods,
known to be capable of solving complex nonlinear problems, data-driven methods
are insufficiently researched in the field of land use. The main objective of this
dissertation is to develop a methodology for predictive urban land use change
models using data-driven approach together with evaluation of the performance of
different data-driven methods, which in the stage of finding patterns of land use
changes use three different machine learning techniques: Decision Trees, Neural
Networks and Support Vector Machines. The proposed methodology of data-driven
methods was presented and special attention was paid to different data
representation, data sampling and the selection of attributes by four methods (χ2,
Info Gain, Gain Ratio and Correlation-based Feature Subset) that best describe the
process of land use change. Additionally, a sensitivity analysis of the Support
Vector Machines -based models was performed with regards to attribute selection
and parameter changes. Development and evaluation of the methodology was
performed using data on three Belgrade municipalities (Zemun, New Belgrade and
Surčin), which are represented as 10×10 m grid cells in four different moments in
time (2001, 2003, 2007 and 2010).
The obtained results indicate that the proposed data-driven methodology provides
predictive models which could be successfully used for creation of possible
scenarios of urban land use changes in the future. All three examined machine
learning techniques are suitable for modeling land use change. Accuracy and
performance of models can be improved using proposed balanced data sampling,
including the information about neighbourhood and history in data
representations and relevant attribute selections. Additionally, using selected
subset of attributes resulted in a simple model and with less possibility to be
overfitted with higher values of Support Vector Machines parameters.
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