Comparison between response surface models and artificial neural networks in hydrologic forecasting

• Main Authors: Yu, Jianjun, Qin, Xiaosheng, Larsen, Ole, Chua, Lloyd Hock Chye
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Civil engineering::Water resources
• Online Access: https://hdl.handle.net/10356/79536; http://hdl.handle.net/10220/19646
• Institution: Nanyang Technological University

Developing an efficient and accurate hydrologic forecasting model is crucial to managing water resources and flooding issues. In this study, response surface (RS) models including multiple linear regression (MLR), quadratic response surface (QRS), and nonlinear response surface (NRS) were applied to...