TY - GEN
T1 - Debris flow hazard assessment based on support vector machine
AU - Yuan, Lifeng
AU - Zhang, Qingfeng
AU - Li, Wenwen
AU - Zou, Lanjun
PY - 2006
Y1 - 2006
N2 - Debris flow hazard assessment is a basic work of hazard monitoring, forecast, alleviation and control. Seven factors, including the maximum volume of once flow (L1), occurrence frequency of debris flow (L2), watershed area (S1), main channel length (S2), watershed relative height difference (S3), valley incision density (S6) and the length ratio of sediment supplement (S9) are chosen as evaluation factors of debris flow hazard degree. Using support vector machine (SVM) theory, 259 basic data of 37 debris flow channels in Yunnan Province are selected as learning samples in this study, then a kind of debris flow hazard assessment model based on SYM is produced. First instance applications gave encouraging results. After Cross Validation test, accuracy of this model came to 70.00%. Through verifying 7 groups of test data, classification accuracy came to 85.71%. The model shows that it has the advantages of best generation, convenience and high precision. SVM is regarded as a broadly applicative tool in debris flow hazard assessment.
AB - Debris flow hazard assessment is a basic work of hazard monitoring, forecast, alleviation and control. Seven factors, including the maximum volume of once flow (L1), occurrence frequency of debris flow (L2), watershed area (S1), main channel length (S2), watershed relative height difference (S3), valley incision density (S6) and the length ratio of sediment supplement (S9) are chosen as evaluation factors of debris flow hazard degree. Using support vector machine (SVM) theory, 259 basic data of 37 debris flow channels in Yunnan Province are selected as learning samples in this study, then a kind of debris flow hazard assessment model based on SYM is produced. First instance applications gave encouraging results. After Cross Validation test, accuracy of this model came to 70.00%. Through verifying 7 groups of test data, classification accuracy came to 85.71%. The model shows that it has the advantages of best generation, convenience and high precision. SVM is regarded as a broadly applicative tool in debris flow hazard assessment.
KW - Debris flow
KW - Hazard assessment
KW - Support Vector Machine (SVM)
UR - http://www.scopus.com/inward/record.url?scp=34948901951&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34948901951&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2006.1083
DO - 10.1109/IGARSS.2006.1083
M3 - Conference contribution
AN - SCOPUS:34948901951
SN - 0780395107
SN - 9780780395107
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4221
EP - 4224
BT - 2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
T2 - 2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Y2 - 31 July 2006 through 4 August 2006
ER -