Open AccessComparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data / Porównanie metod uczenia maszynowego do prognozowania spływu w zlewniach górskich na podstawie ograniczonych danych
Author(s) -
Jan Adamowski,
Shiv O. Prasher
Publication year - 2012
Publication title -
journal of water and land development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.377
H-Index - 16
eISSN - 2083-4535
pISSN - 1429-7426
DOI - 10.2478/v10025-012-0038-4
Subject(s) - surface runoff , watershed , support vector machine , hydrology (agriculture) , precipitation , antecedent moisture , environmental science , runoff curve number , machine learning , meteorology , computer science , geography , engineering , ecology , geotechnical engineering , biology
Runoff forecasting in mountainous regions with processed based models is often difficult and inaccurate due to the complexity of the rainfall-runoff relationships and difficulties involved in obtaining the required data. Machine learning models offer an alternative for runoff forecasting in these regions. This paper explores and compares two machine learning methods, support vector regression (SVR) and wavelet networks (WN) for daily runoff forecasting in the mountainous Sianji watershed located in the Himalayan region of India. The models were based on runoff, antecedent precipitation index, rainfall, and day of the year data collected over the three year period from July 1, 2001 and June 30, 2004. It was found that both the methods provided accurate results, with the best WN model slightly outperforming the best SVR model in accuracy. Both the WN and SVR methods should be tested in other mountainous watershed with limited data to further assess their suitability in forecasting.