FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES¹

Abstract

Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

“…Han et al (2007) compared the RBF and linear kernel functions and found that even within the same catchment, the ideal function can change under different circumstances. Despite being a relatively new approach to hydrological modelling, SVMs and SVRs have been applied to many of the same problems as ANNs, including rainfallrunoff modelling for water resources planning and flood forecasting at various lead-times (Asefa et al, 2006;Han et al, 2007;Behzad et al, 2009;Rasouli et al, 2012), hydraulic modelling (Liong and Sivapragasam, 2002) and downscaling of GCM output (Tripathi et al, 2006). Genetic Programming (GP; Koza, 1992) is another soft computing approach to non-linear modelling and is based on Darwin's theory of evolution by natural selection.…”

Using support vector machines for long-term discharge prediction

“…However, along with the introduction of Vapnik's ε insensitive loss function, SVMs have been extended to solve nonlinear regression estimation (Gunn, 1998;Smola & Schölkopf, 2004) and time series forecasting (Thissen et al, 2003). It is useful to note that the SVM is finding its way into the water sector (Liong & Sivapragasm, 2002;Bray & Han, 2004;Asefa et al, 2004) and a combination of SVM and evolutionary algorithm called EC-SVM has also been attempted recently (Yu et al, 2004). In the latter study, a shuffled complex evolution (SCE-UA) algorithm (Duan et al, 1992(Duan et al, , 1993(Duan et al, , 1994 was used to search phase space parameters (the time delay embedding dimension) and three SVM parameters.…”

Comparative Evaluation of ANN- and SVM-Time Series Models for Predicting Freshwater-Saltwater Interface Fluctuations

“…Thus, it is important to build an adequate direct prediction model that learnt a response function of the given system. To evaluate the stability of the recursive model building, the RP-DP ratio [24] was calculated for T-R-F and T-R-G-F type models with 216 candidate model parameter groups: prediction direct the of RMSE prediction recursive the of RMSE ratio DP RP = − (17) The RP-DP ratio value stands for the extent of the consistency between the direct and recursive prediction models. Thus, a narrower distribution with lower values of the RP-DP ratio indicates a higher possibility that a recursive prediction model of high consistency with a direct prediction model is selected.…”

“…Recently, in the field of hydrology and hydrogeology, research on the application of time series models-based on machine learning techniques such as an artificial neural network (ANN) and a support vector machine (SVM) to prediction of water resources variations-have been increased; Zealand et al [11] utilized the ANN for forecasting short term stream flow of the Winnipeg River system in Canada; Akhtar et al [12] applied ANN to river flow forecasting at Ganges river; Hu et al [13] explored new measures for improving the generalization ability of the ANN for the prediction of the rainfall-runoff; Coulibaly et al [14] and Mohanty et al [15] examined the performance of ANN for the prediction of groundwater level (GWL) fluctuations; Coppola et al [16] used the ANN for the prediction of GWL under variable pumping conditions; Liong and Sivapragasam [17], and Yu et al [18] employed the SVM for the prediction of the flood stage; Asefa et al [19] used the SVM for designing GWL monitoring networks; Gill et al [20] assessed the effect of missing data on the performance of ANN and SVM models for GWL prediction; Yoon et al [21] used ANN and SVM for long-term GWL forecast.…”