Using support vector machines for long-term discharge prediction

TLDR

Accurate time‑ and site‑specific forecasts of streamflow and reservoir inflow are crucial for hydropower reservoir management, and while ARMA models have been the standard, artificial neural networks have recently shown superior efficiency in hydrological prediction. This study presents the support vector machine (SVM) as a promising method for hydrological prediction. The SVM, which relies on structural risk minimization, is trained with parameters optimized by a shuffled complex evolution algorithm and evaluated on long‑term monthly discharge data from the Manwan Hydropower Scheme. When compared with ARMA and ANN models, the SVM demonstrates superior performance, indicating its potential as a candidate for long‑term discharge prediction.

Abstract

Abstract Accurate time- and site-specific forecasts of streamflow and reservoir inflow are important in effective hydropower reservoir management and scheduling. Traditionally, autoregressive moving-average (ARMA) models have been used in modelling water resource time series as a standard representation of stochastic time series. Recently, artificial neural network (ANN) approaches have been proven to be efficient when applied to hydrological prediction. In this paper, the support vector machine (SVM) is presented as a promising method for hydrological prediction. Over-fitting and local optimal solution are unlikely to occur with SVM, which implements the structural risk minimization principle rather than the empirical risk minimization principle. In order to identify appropriate parameters of the SVM prediction model, a shuffled complex evolution algorithm is performed through exponential transformation. The SVM prediction model is tested using the long-term observations of discharges of monthly river flow discharges in the Manwan Hydropower Scheme. Through the comparison of its performance with those of the ARMA and ANN models, it is demonstrated that SVM is a very potential candidate for the prediction of long-term discharges.