A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Combined Model for Water Quality Prediction Based on VMD-TCN-ARIMA Optimized by WSWOA
With environmental degradation and water scarcity becoming increasingly serious, it is urgent to carry out effective management of water resources. The key task of water environment monitoring is to conduct statistics and analysis of changes in water quality characteristics. Aiming to address the problem of the strong fluctuation and strong temporal correlation of water quality characteristics prediction, a new framework for water quality prediction based on variational mode decomposition–temporal convolutional networks–autoregressive integrated moving average (VMD-TCN-ARIMA) optimized by weighted swarm the whale search algorithm (WSWOA) algorithm is proposed. First, the WSWOA was proposed by introducing the two-weighted-factor perturbation strategy and the particle swarm search method based on the whale optimization algorithm (WOA), which effectively improves the convergence speed and global search capabilities. Second, to adaptively decompose the original water quality sequences, the VMD algorithm optimized by WSWOA was utilized, which can extract features and reduce noise in the original sequence. Furthermore, the TCN-ARIMA combined model is proposed for time series analysis. The combined model is introduced to assign different algorithms to the decomposed components to reduce prediction error and modeling effort. In comparison to VMD-TCN model, the experimental results have shown that on the data of water quality characteristic dissolved oxygen (DO), the proposed model’s root mean square error (RMSE) and computational time is reduced by 41.05% and 26.06%, further improving the accuracy and efficiency of prediction.
A Combined Model for Water Quality Prediction Based on VMD-TCN-ARIMA Optimized by WSWOA
With environmental degradation and water scarcity becoming increasingly serious, it is urgent to carry out effective management of water resources. The key task of water environment monitoring is to conduct statistics and analysis of changes in water quality characteristics. Aiming to address the problem of the strong fluctuation and strong temporal correlation of water quality characteristics prediction, a new framework for water quality prediction based on variational mode decomposition–temporal convolutional networks–autoregressive integrated moving average (VMD-TCN-ARIMA) optimized by weighted swarm the whale search algorithm (WSWOA) algorithm is proposed. First, the WSWOA was proposed by introducing the two-weighted-factor perturbation strategy and the particle swarm search method based on the whale optimization algorithm (WOA), which effectively improves the convergence speed and global search capabilities. Second, to adaptively decompose the original water quality sequences, the VMD algorithm optimized by WSWOA was utilized, which can extract features and reduce noise in the original sequence. Furthermore, the TCN-ARIMA combined model is proposed for time series analysis. The combined model is introduced to assign different algorithms to the decomposed components to reduce prediction error and modeling effort. In comparison to VMD-TCN model, the experimental results have shown that on the data of water quality characteristic dissolved oxygen (DO), the proposed model’s root mean square error (RMSE) and computational time is reduced by 41.05% and 26.06%, further improving the accuracy and efficiency of prediction.
A Combined Model for Water Quality Prediction Based on VMD-TCN-ARIMA Optimized by WSWOA
Hongyu Zuo (author) / Xiantai Gou (author) / Xin Wang (author) / Mengyin Zhang (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Multi-scale combined prediction model of concrete dam deformation based on VMD-LSTM-ARIMA
| Springer Verlag | 2024
A Dam Deformation Prediction Model Based on ARIMA-LSTM
| IEEE | 2020