Predicting land use change with data-driven models
Predviđanje promena u korišćenju zemljišta primenom modela vođenih podacima (DATA-DRIVEN MODELS)
Faculty:University of Belgrade, Faculty of Civil Engineering
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- The role and implementation of the national spatial plan and regional development documents in renewal of strategic research, thinking and governance in Serbia (MPNTR-III 47014)
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) thatЈедан 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...
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.View More
Keywords:data-driven modeling; модели вођени подацима (data-driven methods); data mining; machine learning; spatial-temporal modeling; land use changes; Geographic Information Systems; машинско учење; просторно-временско моделирање; промена коришћења земљишта; географски информациони системи