The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.

Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

https://cronfa.swan.ac.uk/Record/cronfa21107/Download/0021107-10052016114550.pdf

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Artificial intelligence based models for stream-flow forecasting: 2000–2015

Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences

High accuracy forecasting of medium and long-term hydrological runoff is beneficial to reservoir operation and management. A hybrid model is proposed for medium and long-term hydrological forecasting in this paper. The hybrid model consists of two methods, Singular Spectrum Analysis (SSA) and Auto Regressive Integrated Moving Average (ARIMA). In this model, the time series of annual runoff are first decomposed into several sub-series corresponding to some tendentious and periodic motions by using SSA and then each sub-series is predicted, respectively, through an appropriate ARIMA model, and lastly a correction procedure is conducted for the sum of the prediction results to ensure the superposed residual to be a pure random series. The annual runoff data of two reservoirs in China are analyzed as case studies. The results have been compared with the predictions made by ARIMA and Singular Spectrum Analysis-Linear Recurrent Formulae (SSA-LRF). It is shown that hybrid model has the best performance.

Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting

Based on wavelet analysis theory, a wavelet predictor-corrector model is developed for the simulation and prediction of monthly discharge time series. In this model, the non-stationary time series of monthly discharge is decomposed into an approximated time series and several stationary detail time series according to the principle of wavelet decomposition. Each one of the decomposed time series is predicted, respectively, through the ARMA model for stationary time series. Then the correction procedure is conducted for the sum of the prediction results. Taking the monthly discharge at Yichang station of Yangtse River as an example, the monthly discharge is simulated by using ARMA model, seasonal ARIMA model, BP artificial neural network model and the wavelet predictor-corrector model proposed in this article, respectively. And the effect of decomposition scale for the wavelet predictor-corrector model is also discussed. It is shown that the wavelet predictor-corrector model has higher prediction accuracy than the some other models and the decomposition scale has no obvious effect on the prediction for monthly discharge time series in the example.

The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition

This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two-stage model for dynamic control of the flood-limited water level (the maximum allowed water level during the flood season, DC-FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC-FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC-FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC-FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the trade-off between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress. The analysis is illustrated via a case study with a real-world reservoir in northeastern China.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015WR017127

An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk

This paper presents a new Two Stage Bayesian Stochastic Dynamic Programming (TS-BSDP) model for real time operation of cascaded hydropower systems to handle varying uncertainty of inflow forecasts from Quantitative Precipitation Forecasts In this model, the inflow forecasts are considered as having increasing uncertainty with extending lead time, thus the forecast horizon is divided into two periods: the inflows in the first period are assumed to be accurate, and the inflows in the second period assumed to be of high uncertainty Two operation strategies are developed to derive hydropower operation policies for the first and the entire forecast horizon using TS-BSDP In this paper, the newly developed model is tested on China's Hun River cascade hydropower system and is compared with three popular stochastic dynamic programming models Comparative results show that the TS-BSDP model exhibits significantly improved system performance in terms of power generation and system reliability due to its explicit and effective utilization of varying degrees of inflow forecast uncertainty The results also show that the decision strategies should be determined considering the magnitude of uncertainty in inflow forecasts Further, this study confirms the previous finding that the benefit in hydropower generation gained from the use of a longer horizon of inflow forecasts is diminished due to higher uncertainty and further reveals that the benefit reduction can be substantially mitigated through explicit consideration of varying magnitudes of forecast uncertainties in the decision-making process

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2013WR015181

Yong Peng

Papers

Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences

Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting

The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition

An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk

A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts