Hydrological Forecasting Using Hybrid Data-Driven Approach

Youngmin Seo,Sungwon Kim

doi:10.3844/ajassp.2016.891.899

Abstract

This study develops a hybrid model, EEMD-FANN, coupling feed Forward Artificial Neural Network (FANN) and Ensemble Empirical Mode Decomposition (EEMD) for improving the accuracy of daily river stage forecasting. An original river stage data is broken down into a residue and Intrinsic Mode Functions (IMFs) using the EEMD and different FANNs are developed as forecasting models for the decomposed IMFs and residue, respectively. The final forecasted time series is produced by the ensemble aggregation of the forecasted IMFs and residue. The efficiency of EEMD-FANN model is assessed based on the comparison with that of single Adaptive Neuro-Fuzzy Inference System (ANFIS) and FANN to demonstrate the applicability of the hybrid approach in daily river stage forecasting. As a result, it is found that the EEMD-FANN model utilizing time series decomposition by the EEMD and ensemble aggregation produces better performance than the single ANFIS and FANN models using original river stage time series as inputs. The results of this study also signify that the approach coupling the EEMD and FANN can significantly enhance the forecasting ability of the single FANN model and can be utilized as an effective modeling methodology to forecast river stage precisely.

Highlights

Forecasting river stage precisely plays an important role for enhancing hydrologic practices such as dam operation, water supply, river management and flood and drought prevention
Original time series gathered from two hydrological observatories was broken down utilizing Ensemble Empirical Mode Decomposition (EEMD) to build up EEMD-Forward Artificial Neural Network (FANN) model
The input variables of FANN models for Intrinsic Mode Functions (IMFs) and a residue were selected based on the optimal lag time determined by statistical correlation analysis utilizing cross-correlation function and partial autocorrelation function according to the previous studies (Sudheer et al, 2002; Shabri and Samsudin, 2015)

Summary

Introduction

Forecasting river stage precisely plays an important role for enhancing hydrologic practices such as dam operation, water supply, river management and flood and drought prevention. Time series decomposition utilizing wavelet and wavelet packet transforms has been known to further improve the forecasting ability of conventional data-driven models (Amiri and Asadi, 2009; Adamowski and Sun, 2010; Gokhale and Khanduja, 2010; Kisi et al, 2011; Nourani et al, 2012; Ravikumar and Tamilselvan, 2014; Seo, 2015; Seo et al, 2015a). Nonlinear data analysis and hybrid model development utilizing Empirical Mode Decomposition (EMD)-based approaches have been successfully performed in various fields. The EMD, which is a selfadaptive and empirical technique to break down a time series, can be utilized to examine nonlinear and nonstationary meteorological and hydrologic data (Tang et al, 2012). The EEMD is a data processing technique based on noise addition (Wu and Huang, 2009) which is devised to improve the EMD. Huang et al (2009) applied EMD and signal analysis based on Hilbert transform (Hilbert spectral analysis) to investigate the characteristics of nonlinear river flow time series. Karthikeyan and Kumar (2013) assessed the forecasting ability of nonstationary time series applying forecasting models which are based on wavelet and EMD. Kisi et al (2014) presented a nonparametric

Methods

Results

Conclusion