Short-term runoff prediction coupling method based on variational mode decomposition and extreme learning machine

• Main Authors: ZHANG XIAO, ZHANG TAO, YANG WENFA, FENG BAOFEI, CHEN YUBIN, NIU WENJING, ZHANG JUN, WANG LE, ZI LI
• Format: Patent
• Language: Chinese; English
• Published: 07.04.2020
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses a short-term runoff prediction coupling method based on variational mode decomposition and an extreme learning machine. The short-term runoff prediction coupling method comprises the following steps: decomposing an original runoff sequence into a plurality of component runoff sequences containing different hydrological characteristic information through a variational mode decomposition method; respectively selecting an influence factor set for each component runoff sequence, then constructing an extreme learning machine model of each component runoff sequence, optimizing calculation parameters of the extreme learning machine model by utilizing a sine and cosine algorithm, and outputting an output value of each extreme learning machine model; and performing superposition operation on the output value, and outputting a prediction result of the original runoff sequence. The short-term runoff prediction coupling method is formed through the variational mode decomposition method, the extrem