1 |
Arsanjani · Helbich and De Noronha Vaz. 2013. Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran. Cities, 32 : 33-42. |
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2 |
Bhatta. 2010. Causes and consequences of urban growth and sprawl. In Analysis of urban growth and sprawl from remote sensing data (pp. 17-36). Springer, Berlin, Heidelberg. |
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3 |
Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm  |
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4 |
GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models  |
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5 |
Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.  |
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6 |
Implementation of a COM-based decision-tree model with VBA in ArcGIS  |
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7 |
An intelligent spatial land use planning support system using socially rational agents  |
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8 |
Jeon, Hong, Lee, Lee and Sung. 2007. Introduction of the New Evaluation Criteria in the Forest Sector of Environmental Conservation Value Map Using LiDAR. Korean Journal of environmental restoration technology. 10(5) : 20-30 (in Korean with English summary) |
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9 |
Jeon, Lee, Song, Sung and Park. 2008. Review of Compositional Evaluation Items for Environmental Conservation Value Assessment Map(ECVAM) of National Land in Korea. Korean Journal of environmental restoration technology. 11(1) : 1-13 (in Korean with English summary). |
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10 |
Jeon, Song, Lee and Kang. 2010. Development Strategy for Utilization of ECVAM using the User Survey. Korean Journal of environmental restoration technology. 15(4) : 111-118 (in Korean with English summary). |
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11 |
Kang and Park. 2000. A study on the urban growth forecasting for the Seoul metropolitan area. The Korean Geographical Society. 35(4) :621-639 (in Korean with English summary). |
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12 |
Kim, Jeon, Song, Kwak and Lee. 2012. Application of ECVAM as a Indicator for Monitoring National Environment in Korea. Korean Journal of environmental restoration technology. 11(2) : 3-16 (in Korean with English summary). |
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13 |
An investigation on the conditions of pruning an induced decision tree  |
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14 |
Kim, Lee, Jung and Jung. 2016. Mapping the Assessment of Natural environment Outstanding Areas of North Korea Using Logistic Regression Analysis. Journal of the Korean Cartographic Associ- ation. 16(3) :75-88 |
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15 |
Urban growth, climate change, and freshwater availability.  |
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16 |
Post-pruning in decision tree induction using multiple performance measures  |
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17 |
A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis  |
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18 |
Son·Jeon and Choi. 2009. GIS and statistical techniques used in Korea urban expansion trend analysis. Korean Society for Geospatial Information Science. 17(4) : 13-22(in English with Korean summary). |
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19 |
Song, Kim, Jeon, Park and Lee. 2012. Improvement of the Criteria on Naturalness of the Environmental Conservation Value Assessment Map (ECVAM). Korean Journal of environmental restoration technology. 15(2) : 31-40 (in Korean with English summary). |
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20 |
Urban Growth Prediction: A Review of Computational Models and Human Perceptions  |
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21 |
A random forest classifier based on pixel comparison features for urban LiDAR data  |
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22 |
Won and Hwang. 2018. Simulating Land Use Change Using Decision Tree and SVM Model : A Case Study of North Korea's City after the Unification. The Korea Spatial Planning Review. 97 : 41-56 (in Korean with English summary). |
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23 |
Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping  |
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