2 research outputs found
Potential of support-vector regression for forecasting stream flow
Vodotok je važan za hidroloÅ”ko prouÄavanje zato Å”to odreÄuje varijabilnost vode i magnitudu rijeke. Inženjerstvo vodnih resursa uvijek se bavi povijesnim podacima i pokuÅ”ava procijeniti prognostiÄke podatke kako bi se osiguralo bolje predviÄanje za primjenu kod bilo kojeg vodnog resursa, na pr. projektiranja vodnog potencijala brane hidroelektrana, procjene niskog protoka, i održavanja zalihe vode. U radu se predstavljaju tri raÄunalna programa za primjenu kod rjeÅ”avanja ovakvih sadržaja, tj. umjetne neuronske mreže - artificial neural networks (ANNs), prilagodljivi sustavi neuro-neizrazitog zakljuÄivanja - adaptive-neuro-fuzzy inference systems (ANFISs), i support vector machines (SVMs). Za stvaranje procjene koriÅ”tena je Rijeka Telom, smjeÅ”tena u Cameron Highlands distriktu Pahanga, Malaysia. Podaci o dnevnom prosjeÄnom protoku rijeke Telom, kao Å”to su koliÄina padavina i podaci o vodostaju, koristili su se za period od ožujka 1984. do sijeÄnja 2013. za poduÄavanje, ispitivanje i ocjenjivanje izabranih modela. SVM pristup je dao bolje rezultate nego ANFIS i ANNs kod procjenjivanja dnevne prosjeÄne fluktuacije vodotoka.Stream flow is an important input for hydrology studies because it determines the water variability and magnitude of a river. Water resources engineering always deals with historical data and tries to estimate the forecasting records in order to give a better prediction for any water resources applications, such as designing the water potential of hydroelectric dams, estimating low flow, and maintaining the water supply. This paper presents three soft-computing approaches for dealing with these issues, i.e. artificial neural networks (ANNs), adaptive-neuro-fuzzy inference systems (ANFISs), and support vector machines (SVMs). Telom River, located in the Cameron Highlands district of Pahang, Malaysia, was used in making the estimation. The Telom Riverās daily mean discharge records, such as rainfall and river-level data, were used for the period of March 1984 ā January 2013 for training, testing, and validating the selected models. The SVM approach provided better results than ANFIS and ANNs in estimating the daily mean fluctuation of the streamās flow