Articles | Volume 20, issue 8
https://doi.org/10.5194/hess-20-3379-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-3379-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Aug 2016
Research article |
| 23 Aug 2016
Case-based knowledge formalization and reasoning method for digital terrain analysis – application to extracting drainage networks
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, 100101 Beijing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, 210023 Nanjing, China
College of Resources and Environment, University of Chinese Academy of Sciences, 100049 Beijing, China
Xue-Wei Wu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, 100101 Beijing, China
College of Resources and Environment, University of Chinese Academy of Sciences, 100049 Beijing, China
Jing-Chao Jiang
Smart City Research Center, Hangzhou Dianzi University, 310012 Hangzhou, China
A-Xing Zhu
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, 100101 Beijing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, 210023 Nanjing, China
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
Key Laboratory of Virtual Geographic Environment, Ministry of Education, 210023 Nanjing, China
Viewed
Total article views: 3,972 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 19 Jan 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,474 | 1,330 | 168 | 3,972 | 1,020 | 188 | 224 |
- HTML: 2,474
- PDF: 1,330
- XML: 168
- Total: 3,972
- Supplement: 1,020
- BibTeX: 188
- EndNote: 224
Total article views: 3,338 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 Aug 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,122 | 1,055 | 161 | 3,338 | 796 | 178 | 213 |
- HTML: 2,122
- PDF: 1,055
- XML: 161
- Total: 3,338
- Supplement: 796
- BibTeX: 178
- EndNote: 213
Total article views: 634 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 19 Jan 2016)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 352 | 275 | 7 | 634 | 224 | 10 | 11 |
- HTML: 352
- PDF: 275
- XML: 7
- Total: 634
- Supplement: 224
- BibTeX: 10
- EndNote: 11
Cited
19 citations as recorded by crossref.
- Geomorphometry today I. Florinsky https://doi.org/10.35595/2414-9179-2021-2-27-394-448
- Spatial optimization of watershed best management practice scenarios based on boundary-adaptive configuration units L. Zhu et al. https://doi.org/10.1177/0309133320939002
- A case-based method of selecting covariates for digital soil mapping P. LIANG et al. https://doi.org/10.1016/S2095-3119(19)62857-1
- Bibliometric Analysis on the Research of Geoscience Knowledge Graph (GeoKG) from 2012 to 2023 Z. Hou et al. https://doi.org/10.3390/ijgi13070255
- Quantifying the mixed uncertainty for calculating slope from a gridded digital elevation model J. Zhang et al. https://doi.org/10.1080/10106049.2025.2480307
- GIScience and remote sensing in natural resource and environmental research: Status quo and future perspectives T. Pei et al. https://doi.org/10.1016/j.geosus.2021.08.004
- Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs W. Zhang et al. https://doi.org/10.3390/rs13112024
- Next generation of GIS: must be easy A. Zhu et al. https://doi.org/10.1080/19475683.2020.1766563
- Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method Z. Hou et al. https://doi.org/10.3390/ijgi10090605
- Comparison on two case‐based reasoning strategies of automatically selecting terrain covariates for digital soil mapping P. Liang et al. https://doi.org/10.1111/tgis.12831
- Review on algorithms of dealing with depressions in grid DEM Y. Wang et al. https://doi.org/10.1080/19475683.2019.1604571
- Unsupervised active–transfer learning for automated landslide mapping Z. Wang & A. Brenning https://doi.org/10.1016/j.cageo.2023.105457
- Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning Z. Wang et al. https://doi.org/10.5194/gmd-15-8765-2022
- A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction Y. Wang et al. https://doi.org/10.1080/19475683.2024.2324398
- Position paper: Domain knowledge-driven intelligentization of watershed modeling and scenario analysis for precise watershed management C. Qin et al. https://doi.org/10.1016/j.infgeo.2025.100016
- Using the most similar case method to automatically select environmental covariates for predictive mapping P. Liang et al. https://doi.org/10.1007/s12145-020-00466-5
- Mapping Surficial Soil Particle Size Fractions in Alpine Permafrost Regions of the Qinghai–Tibet Plateau C. Wang et al. https://doi.org/10.3390/rs13071392
- Intelligent determination of proper spatial extents for input data during geographical model workflow building Z. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106369
- A Five-Star Guide for Achieving Replicability and Reproducibility When Working with GIS Software and Algorithms J. Wilson et al. https://doi.org/10.1080/24694452.2020.1806026
19 citations as recorded by crossref.
- Geomorphometry today I. Florinsky https://doi.org/10.35595/2414-9179-2021-2-27-394-448
- Spatial optimization of watershed best management practice scenarios based on boundary-adaptive configuration units L. Zhu et al. https://doi.org/10.1177/0309133320939002
- A case-based method of selecting covariates for digital soil mapping P. LIANG et al. https://doi.org/10.1016/S2095-3119(19)62857-1
- Bibliometric Analysis on the Research of Geoscience Knowledge Graph (GeoKG) from 2012 to 2023 Z. Hou et al. https://doi.org/10.3390/ijgi13070255
- Quantifying the mixed uncertainty for calculating slope from a gridded digital elevation model J. Zhang et al. https://doi.org/10.1080/10106049.2025.2480307
- GIScience and remote sensing in natural resource and environmental research: Status quo and future perspectives T. Pei et al. https://doi.org/10.1016/j.geosus.2021.08.004
- Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs W. Zhang et al. https://doi.org/10.3390/rs13112024
- Next generation of GIS: must be easy A. Zhu et al. https://doi.org/10.1080/19475683.2020.1766563
- Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method Z. Hou et al. https://doi.org/10.3390/ijgi10090605
- Comparison on two case‐based reasoning strategies of automatically selecting terrain covariates for digital soil mapping P. Liang et al. https://doi.org/10.1111/tgis.12831
- Review on algorithms of dealing with depressions in grid DEM Y. Wang et al. https://doi.org/10.1080/19475683.2019.1604571
- Unsupervised active–transfer learning for automated landslide mapping Z. Wang & A. Brenning https://doi.org/10.1016/j.cageo.2023.105457
- Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning Z. Wang et al. https://doi.org/10.5194/gmd-15-8765-2022
- A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction Y. Wang et al. https://doi.org/10.1080/19475683.2024.2324398
- Position paper: Domain knowledge-driven intelligentization of watershed modeling and scenario analysis for precise watershed management C. Qin et al. https://doi.org/10.1016/j.infgeo.2025.100016
- Using the most similar case method to automatically select environmental covariates for predictive mapping P. Liang et al. https://doi.org/10.1007/s12145-020-00466-5
- Mapping Surficial Soil Particle Size Fractions in Alpine Permafrost Regions of the Qinghai–Tibet Plateau C. Wang et al. https://doi.org/10.3390/rs13071392
- Intelligent determination of proper spatial extents for input data during geographical model workflow building Z. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106369
- A Five-Star Guide for Achieving Replicability and Reproducibility When Working with GIS Software and Algorithms J. Wilson et al. https://doi.org/10.1080/24694452.2020.1806026
Saved (final revised paper)
Latest update: 11 Sep 2026
Short summary
Application of digital terrain analysis (DTA), which is typically a modeling process involving workflow building, relies heavily on DTA domain knowledge. However, the DTA knowledge has not been formalized well to be available for inference in automatic tools. We propose a case-based methodology to solve this problem. This methodology can also be applied to other domains of geographical modeling with a similar situation.
Application of digital terrain analysis (DTA), which is typically a modeling process involving...
