Model and application of annual river runoff prediction based on complementary set empirical mode decomposition combined with particle swarm optimization adaptive neuro-fuzzy system
Open Access
- 14 March 2023
- journal article
- research article
- Published by IWA Publishing in Water Supply
- Vol. 23 (5), 1760-1774
- https://doi.org/10.2166/ws.2023.075
Abstract
Runoff is affected by natural and nonnatural factors in the process of formation, and the runoff series is generally nonstationary time series. How to improve the accuracy of runoff prediction has always been a difficult problem for hydrologists. The key to solve this problem is to reduce the complexity of runoff series and improve the accuracy of runoff prediction model. Based on the aforementioned ideas, this article uses the complementary set empirical mode decomposition to decompose the runoff series into multiple intrinsic components that retain time–frequency information, thus reducing the complexity of the runoff series. The particle swarm optimization (PSO) adaptive neuro-fuzzy system is used to predict each intrinsic component to improve the accuracy of runoff prediction. After that, the trained intrinsic components of the model are reconstructed into the original runoff series. The example shows that the absolute relative error of the runoff forecasting model constructed in this article is 0.039, and the determination coefficient is 0.973. This model can be applied to the annual runoff series forecasting. Comparing the prediction results of this model with empirical mode decomposition algorithm-ANFIS model and ANFIS model, complementary set empirical mode decomposition algorithm-PSO-ANFIS model shows obvious advantages.Keywords
Funding Information
- Natural Science Foundation of Zhejiang Province (No.LZJWY22E090005)
- Zhejiang Tongji Vocational College of science and Technology (No. FRF21PY001)
- Science and Technology Program of Zhejiang Province (No.RC2110)
- Science and Technology Program of Zhejiang Province (No.RC2031)
This publication has 17 references indexed in Scilit:
- Predicting relative energy dissipation for vertical drops equipped with a horizontal screen using soft computing techniquesWater Supply, 2021
- EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictionsWater Supply, 2021
- A Hyperspectral Image Classification Method Using Multifeature Vectors and Optimized KELMIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
- A combined prediction approach based on wavelet transform for crop water requirementWater Supply, 2020
- Wavelet based relevance vector machine model for monthly runoff predictionWater Quality Research Journal, 2018
- Uncertainty analysis and prediction of river runoff with multi-time scalesWater Supply, 2016
- Hybrid wavelet-GMDH model to forecast significant wave heightWater Supply, 2015
- A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff processJournal of Hydrology, 2013
- Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modelingJournal of Hydrology, 2010
- COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOVEL NOISE ENHANCED DATA ANALYSIS METHODAdvances in Adaptive Data Analysis, 2010