000			01932nam a22002417a 4500
003			ZW-GwMSU
005			20230424100156.0
008			230424b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_aWAN, Shiuan
245			_aEntropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping
264			_aVerlag _bSpringer _c2013
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aEnvironmental earth sciences _vVolume , number ,
520			_aGeneration of landslide susceptibility maps is important for engineering geologists and geomorphologists. The goal of this study is to generate a reliable susceptibility map based on digital elevation modeling and remote sensing data through clustering technique. This study focused on the landslide problems on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Two stages of analysis were used to extract the dominant attributes and thresholds: (1) calculate the entropy with regard to the measure of influenced variables to the occurrence of landslide and (2) use the clustering analysis K-means with particle swarm optimization (KPSO) to resolve the difficulties in creating landslide susceptibility maps. The knowledge scope with regard to core factors and thresholds are solved. The self-organization map (SOM) is used as a parallel study for comparison. The overall accuracy of the susceptibility map is 86 and 77 % for KPSO and SOM, respectively. Then, the susceptibility maps are drawn and verifications made. The generation of a susceptibility map is useful for decision makers and managers to handle the landslide risk area.
650			_alandslide susceptibility (potential) maps
650			_adata mining
650			_aparticle swam optimisation
856			_uhttps://doi.org/10.1007/s12665-012-1832-7
942			_2lcc _cJA
999			_c161860 _d161860