A probabilistic wavelet-support vector regression model for streamflow forecasting with rainfall and climate information input

2018-11-23 本站

题目：A probabilistic wavelet-support vector regression model for streamflow forecasting with rainfall and climate information input

期刊：Journal of Hydrometeorology

作者：Zhiyong Liu1，Ping Zhou2，Yinqin Zhang3

单位：1.Institute of Geography, Heidelberg University, Heidelberg, Germany；2.Department of Forest Ecology, Guangdong Academy of Forestry, Guangzhou, China；3.College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China, and Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, Indiana

摘要：It is essential to explore reliable streamflow forecasting techniques for water resources management. In this study, a Bayesian-wavelet-support vector regression model (BWS model) is developed for one- and multi-step-ahead streamflow forecasting using local meteo-hydrological observations and climate indices including the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) as potential predictors. To accomplish this, a two-step strategy is applied. In the first step, the discrete wavelet 美高梅 is coupled with a support vector regression model for streamflow prediction. The three key factors of mother wavelets, decomposition levels, and edge effects are considered in the wavelet decomposition phase when using the hybrid wavelet-support vector regression model (WS model). Different combinations of these factors form a variety of WS models with corresponding forecasts. The second step combines multiple candidate WS models with “good” performance via Bayesian model averaging. This integrates the predictive strengths of different candidate WS models, giving a realistic assessment of the predictive uncertainty. The new ensemble model is used to forecast daily and monthly streamflows at two sites in Dongjiang Basin, South China. The results show that the proposed BWS model consistently generates more reliable predictions for daily (lead times of 1–7 days) and monthly (lead times of 1–3 months) forecasts as compared with the best single-member WS models and the adaptive neural-fuzzy inference system (ANFIS). Furthermore, the proposed BWS model provides detailed information about the predictive uncertainty.

关键词：Runoff，Bayesian methods， Neural networks， Ensembles，Forecasting;，Probability forecasts/models/distribution