Urban Sprawl prediction in 2030 using decision tree |
Kim, Geun-Han
(Korea Environment Institute Division for Environmental Planning)
Choi, Hee-Sun (Korea Environment Institute Division for Environmental Planning) Kim, Dong-Beom (Kongju National University Department of Geography) Jung, Yee-Rim (Seoul National University Graduate School of Environmental Studies) Jin, Dae-Yong (Korea Environment Institute, Center for Environmental Data Strategy) |
1 | 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). DOI |
2 | Wu, Ke, Chen, Liang, Zhao and Hong. 2020. Application of alternating decision tree with adaboost and bagging ensembles for landslide susceptibility mapping. CATENA, 187, 104396. DOI |
3 | 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. DOI |
4 | 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. |
5 | Cheng.Wang and Zhang. 2010. Implementation of a COM-based decision-tree model with VBA in ArcGIS. Expert Systems with Applications, 37(1) : 12-17. DOI |
6 | Bui..Nguyen and Choi. 2020. Prediction of slope failure in open-pit mines using a novel hybrid artifcial intelligence model based on decision tree and evolution algorithm. Scientific Reports. 10(1) : 1-17. DOI |
7 | Chen.Li.Hou.Wang.Wang.Panahi.Li. Peng.Guo and Niu. 2018. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Science of The Total Environment. 634 : 853-867. DOI |
8 | Chen.Li.Xue.Shahabi.Li.Hong.Wan g.Bian.Zhang and Pradhan. 2020. Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. Science of The Total Environment. 701, 134979. DOI |
9 | Ghavami.Taleai and Arentze. 2017. An intelligent spatial land use planning support system using socially rational agents. International Journal of Geographical Information Science. 31(5) : 1022-1041. DOI |
10 | 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) |
11 | 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). |
12 | Kim and Koehler. 1994. An investigation on the conditions of pruning an induced decision tree. European Journal of Operational Research, 77(1) : 82-95 (in Korean with English summary). DOI |
13 | 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). |
14 | 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). |
15 | 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). |
16 | 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 Association. 16(3) : 75-88 DOI |
17 | Mcdonald.Green.Balk.Fekete.Revenga. Todd and Montgomery. 2011. Urban growth, climate change, and freshwater availability. Proceedings of the National Academy of Sciences. 108(15) : 6312-6317. DOI |
18 | Osei-Bryson. 2007. Post-pruning in decision tree induction using multiple performance measures. Computers & operations research, 34(11) : 3331-3345. DOI |
19 | 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). |
20 | Ruiz Hernandez and Shi. 2018. A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis. International journal of remote sensing, 39(4) : 1175-1198. DOI |
21 | 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). DOI |
22 | Triantakonstantis and Mountrakis. 2012. Urban growth prediction: a review of computational models and human perceptions. Journal of Geographic Information System. 4 : 555-587. DOI |
23 | Wang, Shu, Wang, Guo, Liu and Li. 2019. A random forest classifier based on pixel comparison features for urban LiDAR data. ISPRS journal of photogrammetry and remote sensing, 148 : 75-86. DOI |