A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network
The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development o...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2020/8828664 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832555008575406080 |
|---|---|
| author | Muhammad Sibtain Xianshan Li Snoober Saleem |
| author_facet | Muhammad Sibtain Xianshan Li Snoober Saleem |
| author_sort | Muhammad Sibtain |
| collection | DOAJ |
| description | The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction. |
| format | Article |
| id | doaj-art-a874b061525e40a0b559097663532ef5 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-a874b061525e40a0b559097663532ef52025-02-03T05:49:53ZengWileyAdvances in Meteorology1687-93091687-93172020-01-01202010.1155/2020/88286648828664A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning NetworkMuhammad Sibtain0Xianshan Li1Snoober Saleem2Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 44302, ChinaLaboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 44302, ChinaDepartment of Economics, National University of Modern Languages, Islamabad 44000, PakistanThe accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction.http://dx.doi.org/10.1155/2020/8828664 |
| spellingShingle | Muhammad Sibtain Xianshan Li Snoober Saleem A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network Advances in Meteorology |
| title | A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network |
| title_full | A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network |
| title_fullStr | A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network |
| title_full_unstemmed | A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network |
| title_short | A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network |
| title_sort | multivariate and multistage medium and long term streamflow prediction based on an ensemble of signal decomposition techniques with a deep learning network |
| url | http://dx.doi.org/10.1155/2020/8828664 |
| work_keys_str_mv | AT muhammadsibtain amultivariateandmultistagemediumandlongtermstreamflowpredictionbasedonanensembleofsignaldecompositiontechniqueswithadeeplearningnetwork AT xianshanli amultivariateandmultistagemediumandlongtermstreamflowpredictionbasedonanensembleofsignaldecompositiontechniqueswithadeeplearningnetwork AT snoobersaleem amultivariateandmultistagemediumandlongtermstreamflowpredictionbasedonanensembleofsignaldecompositiontechniqueswithadeeplearningnetwork AT muhammadsibtain multivariateandmultistagemediumandlongtermstreamflowpredictionbasedonanensembleofsignaldecompositiontechniqueswithadeeplearningnetwork AT xianshanli multivariateandmultistagemediumandlongtermstreamflowpredictionbasedonanensembleofsignaldecompositiontechniqueswithadeeplearningnetwork AT snoobersaleem multivariateandmultistagemediumandlongtermstreamflowpredictionbasedonanensembleofsignaldecompositiontechniqueswithadeeplearningnetwork |