Integrating Support Vector Regression and a geomorphologic Artificial Neural Network for daily rainfall-runoff modeling
- 1 January 2016
- journal article
- research article
- Published by Elsevier BV in Applied Soft Computing
- Vol. 38, 329-345
- https://doi.org/10.1016/j.asoc.2015.09.049
Abstract
No abstract availableFunding Information
- University of Tehran (28738/1/1)
This publication has 55 references indexed in Scilit:
- Modeling rainfall-runoff process using soft computing techniquesComputers & Geosciences, 2013
- Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approachJournal of Hydrology, 2010
- Data-driven modelling: some past experiences and new approachesJournal of Hydroinformatics, 2008
- Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniquesWater Resources Research, 2004
- Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchmentsJournal of Hydrology, 2001
- Comparison of ANNs and Empirical Approaches for Predicting Watershed RunoffJournal of Water Resources Planning and Management, 2000
- Artificial Neural Networks in Hydrology. I: Preliminary ConceptsJournal of Hydrologic Engineering, 2000
- Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of informationWater Resources Research, 1998
- Comparison of simple versus complex distributed runoff models on a midsized semiarid watershedWater Resources Research, 1994
- Changing ideas in hydrology — The case of physically-based modelsJournal of Hydrology, 1989