A flood inundation modelling using v-support vector machine regression model. (November 2015)

• Record Type: Journal Article
• Authors: Liu, Yang; Pender, Gareth
• Is Part Of: Engineering applications of artificial intelligence. Volume 46:Part A(2015:Oct.)
• Page Start: 223
• Page End: 231
• Publication Date: 2015-11
• Subjects: Support vector machine regression -- Linear regression -- Simulation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.09.014
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Full two dimensional (2D) hydrodynamic models have proven to be successful in a wide area of applications. The limitation of using full 2D models is their expensive computational requirement. The flood risk analysis and model uncertainty analysis usually need to run the numerical model and evaluate the performance thousands of times. However, in real world applications, there is simply not enough time and resources to perform such a huge number of model runs. In this study, a computational framework, known as v-Support Vector Regression (SVR)-Fine Grid Model (FGM) or linear regression (LR)-FGM, is presented for solving computationally expensive simulation problems. The concept of v-SVR-FGM or LR-FGM will be demonstrated via a small number of fine grid model (FGM) runs using a nonlinear regression or linear regression model with data preprocessing. The approximation model is performed in predicting the form of results of FGM instead of running the time consuming FGM. This approach can substantially reduce computational running time without loss of accuracy of FGM. The simulation results suggest that the proposed method is able to achieve good predictive results (water depth and velocity) as well as provide considerable savings in computer time. Highlights: A computational framework is presented for solving time consuming problems. Support vector machine (SVM) for regression or linear regression are applied. The approximation is performed in predicting the results of fine grid model. This approach can substantially reduce computational running time. The proposed method is also able to achieve good predictive results.