New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes
Reference evapotranspiration (ETo) is a vital climate parameter affecting plants' water use. ETo can generate large deficits in soil moisture and runoff in different regions and seasons, leading to uncertainties in drought warning systems. A novel multivariate variational mode decomposition int...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-11-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X2301172X |
| _version_ | 1797655119736602624 |
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| author | Mumtaz Ali Mehdi Jamei Ramendra Prasad Masoud Karbasi Yong Xiang Borui Cai Shahab Abdulla Aitazaz Ahsan Farooque Abdulhaleem H. Labban |
| author_facet | Mumtaz Ali Mehdi Jamei Ramendra Prasad Masoud Karbasi Yong Xiang Borui Cai Shahab Abdulla Aitazaz Ahsan Farooque Abdulhaleem H. Labban |
| author_sort | Mumtaz Ali |
| collection | DOAJ |
| description | Reference evapotranspiration (ETo) is a vital climate parameter affecting plants' water use. ETo can generate large deficits in soil moisture and runoff in different regions and seasons, leading to uncertainties in drought warning systems. A novel multivariate variational mode decomposition integrated with a boosted regression tree (i.e., MVMD-BRT) is constructed to forecast daily ETo. Firstly, the correlation matrix based on cross-correlation was computed to investigate the significant input predictor lags of daily ETo. Secondly, the MVMD technique decomposes the significant input lags into signals called intrinsic mode functions (IMFs). Thirdly, the IMFs were then employed in the BRT to build the MVMD-BRT model for daily ETo forecasting. A comparative assessment of MVMD against multivariate empirical mode decomposition (MEMD) was also performed on the same lines to develop the MEMD-BRT model. The MVMD-BRT model is compared against the random forest (RF) and hybrid MVMD-RF, MEMD-RF, extreme learning machine (ELM), and hybrid MVMD-ELM, MEMD-ELM, and cascaded feedforward neural network (CFNN) along with its hybrid MVMD-CFNN models for two stations in Queensland, Australia using a set of goodness-of-fit metrics. The results prove that the MVMD-BRT provide accurate daily ETo forecasting against the benchmark models. The MVMD-BRT model yielded the highest accuracy in terms of (WIE = 0.9070, NSE = 0.8421, LME = 0.6529, KGE = 0.8792) and (WIE = 0.8966, NSE = 0.8396, LME = 0.6521, KGE = 0.8803) for Brisbane and Gympie stations against the comparing models. |
| first_indexed | 2024-03-11T17:09:32Z |
| format | Article |
| id | doaj.art-97fb176451c54bbc90b75db264e50424 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-03-11T17:09:32Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-97fb176451c54bbc90b75db264e504242023-10-20T06:38:48ZengElsevierEcological Indicators1470-160X2023-11-01155111030New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemesMumtaz Ali0Mehdi Jamei1Ramendra Prasad2Masoud Karbasi3Yong Xiang4Borui Cai5Shahab Abdulla6Aitazaz Ahsan Farooque7Abdulhaleem H. Labban8UniSQ College, University of Southern Queensland 4350 QLD, Australia; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq; Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, Canada; Corresponding authors at: UniSQ College, University of Southern Queensland 4350 QLD, Australia (M. Ali); Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, Canada (M. Jamei).Faculty of Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan, Iran; Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, Canada; Corresponding authors at: UniSQ College, University of Southern Queensland 4350 QLD, Australia (M. Ali); Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, Canada (M. Jamei).Department of Science, School of Science and Technology, The University of Fiji, Saweni, Lautoka, FijiWater Engineering Department, Faculty of Agriculture, University of Zanjan, Zanjan, Iran; Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, CanadaSchool of Information Technology, Deakin University, VIC 3125, AustraliaSchool of Information Technology, Deakin University, VIC 3125, AustraliaUniSQ College, University of Southern Queensland 4350 QLD, Australia; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, IraqFaculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, Canada; Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, CanadaCentre of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaReference evapotranspiration (ETo) is a vital climate parameter affecting plants' water use. ETo can generate large deficits in soil moisture and runoff in different regions and seasons, leading to uncertainties in drought warning systems. A novel multivariate variational mode decomposition integrated with a boosted regression tree (i.e., MVMD-BRT) is constructed to forecast daily ETo. Firstly, the correlation matrix based on cross-correlation was computed to investigate the significant input predictor lags of daily ETo. Secondly, the MVMD technique decomposes the significant input lags into signals called intrinsic mode functions (IMFs). Thirdly, the IMFs were then employed in the BRT to build the MVMD-BRT model for daily ETo forecasting. A comparative assessment of MVMD against multivariate empirical mode decomposition (MEMD) was also performed on the same lines to develop the MEMD-BRT model. The MVMD-BRT model is compared against the random forest (RF) and hybrid MVMD-RF, MEMD-RF, extreme learning machine (ELM), and hybrid MVMD-ELM, MEMD-ELM, and cascaded feedforward neural network (CFNN) along with its hybrid MVMD-CFNN models for two stations in Queensland, Australia using a set of goodness-of-fit metrics. The results prove that the MVMD-BRT provide accurate daily ETo forecasting against the benchmark models. The MVMD-BRT model yielded the highest accuracy in terms of (WIE = 0.9070, NSE = 0.8421, LME = 0.6529, KGE = 0.8792) and (WIE = 0.8966, NSE = 0.8396, LME = 0.6521, KGE = 0.8803) for Brisbane and Gympie stations against the comparing models.http://www.sciencedirect.com/science/article/pii/S1470160X2301172XReference evapotranspirationMultivariate variational mode decompositionMultivariate empirical mode decompositionBoosted regression tree |
| spellingShingle | Mumtaz Ali Mehdi Jamei Ramendra Prasad Masoud Karbasi Yong Xiang Borui Cai Shahab Abdulla Aitazaz Ahsan Farooque Abdulhaleem H. Labban New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes Ecological Indicators Reference evapotranspiration Multivariate variational mode decomposition Multivariate empirical mode decomposition Boosted regression tree |
| title | New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes |
| title_full | New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes |
| title_fullStr | New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes |
| title_full_unstemmed | New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes |
| title_short | New achievements on daily reference evapotranspiration forecasting: Potential assessment of multivariate signal decomposition schemes |
| title_sort | new achievements on daily reference evapotranspiration forecasting potential assessment of multivariate signal decomposition schemes |
| topic | Reference evapotranspiration Multivariate variational mode decomposition Multivariate empirical mode decomposition Boosted regression tree |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X2301172X |
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