1 research outputs found
Suspended sediment modelling by SVM and wavelet
Napredak koji danas bilježimo u primjeni umjetne inteligencije za predviÄanje hidroloÅ”kih dogaÄaja doveo je do brojnih promjena u sferi predviÄanja. ValiÄni model baziran na metodi potpornih vektora (WSVM) dobiven je spajanjem valiÄne analize i metode potpornih vektora (SVM). Za uÄenje i testiranje koriÅ”teni su podaci o lebdeÄem nanosu (SS) i dnevnom protoku (Q) izmjereni na rijeci Iowa u SAD-u. Provedene analize su pokazale da se valiÄni model WSVM može koristiti za aproksimaciju koliÄine lebdeÄeg nanosa.Present-day advances in artificial intelligence, as a forecaster for hydrological events, have led to numerous changes in forecasting. The wavelet support vector machine (WSWM) model is achieved by conjunction of the wavelet analysis and the support vector machine (SVM). The suspended sediment (SS) and daily stream flow (Q) data from the Iowa River in the USA were used for training and testing. The WSVM could logically be used for approximation of the suspended sediment load