http://rdf.ncbi.nlm.nih.gov/pubchem/patent/KR-102145250-B1
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q50-10 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q50-26 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N99-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q50-26 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06Q50-10 |
| filingDate | 2018-10-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2020-08-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2020-08-18-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | KR-102145250-B1 |
| titleOfInvention | Method and apparatus for landslide susceptibility mapping using machine-learning architecture |
| abstract | In the method of creating a landslide vulnerability map of the present invention, receiving data from a preliminary survey result for a target area for creating a landslide vulnerability map, selecting an environmental factor necessary to calculate the landslide vulnerability, and the preliminary survey result data and the above Considering environmental factors, establishing a landslide vulnerability model using a machine learning algorithm, and creating a landslide vulnerability map of the target area based on this. According to the present invention, it is possible to improve accuracy and prediction performance by applying various machine learning techniques in creating a landslide vulnerability map. |
| priorityDate | 2018-10-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID962 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419512635 |
Total number of triples: 16.