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Dugorocno planiranje hidrotehnickih sustava zahtijeva poznavanje dugorocne dostupnosti vode, najcesce u obliku srednjeg mjesecnog protoka. Uglavnom se koriste znanja iz stohasticke hidrologije, a moguci scenariji dobivaju se generiranjem sintetickog protoka. Raspolaganje klimatskim modelima namece mogucnost modeliranja iz buducih scenarija, a pretpostavka u radu je da se za tu svrhu može primjenjivati nadzirano ucenje. U radu je analizirana preciznost tri modela nadziranog ucenja u tri pristupa i autoregresivnog modela u prvom pristupu, za predviđanje srednjeg mjesecnog protoka, a u ovisnosti o duljini povijesnog niza.

Comparison of supervised learning methods for prediction of monthly average flow