Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition
In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and long short-term memory (LSTM), were used along with adaptive boosting and general regression neural network to forecast multi-step-ahead pan evaporation in two arid climate stations in Iran (Ahvaz and Yazd...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823010499 |
| _version_ | 1827375597759758336 |
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| author | Masoud Karbasi Mumtaz Ali Sayed M. Bateni Changhyun Jun Mehdi Jamei Zaher Mundher Yaseen |
| author_facet | Masoud Karbasi Mumtaz Ali Sayed M. Bateni Changhyun Jun Mehdi Jamei Zaher Mundher Yaseen |
| author_sort | Masoud Karbasi |
| collection | DOAJ |
| description | In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and long short-term memory (LSTM), were used along with adaptive boosting and general regression neural network to forecast multi-step-ahead pan evaporation in two arid climate stations in Iran (Ahvaz and Yazd). Lagged time series of meteorological data and pan evaporation data were input to the machine learning models. Two feature selection methods, i.e., the Boruta extra tree and XGBoost, were used to select significant inputs to reduce the number of inputs and model complexity. Different statistical metrics were used to investigate the model performance. The results demonstrated that Boruta-extra-tree-based models were more accurate than XGBoost-based models. Compared with the machine learning techniques, the combination of Boruta extra tree and BiLSTM enabled more accurate one-day-ahead forecasting of pan evaporation for both sites (Root Mean Square Error (RMSE) = 1.6857, for the Ahvaz station, and RMSE = 1.3996 for the Yazd station). The proposed model was used to forecast up to 30 days ahead of pan evaporation in both stations. The results showed that the Boruta-BiLSTM model could accurately forecast the pan evaporation for up to 30 days in both stations. |
| first_indexed | 2024-03-08T11:54:20Z |
| format | Article |
| id | doaj.art-4e71e2be923f449ba9267c4458e2500b |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-08T11:54:20Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-4e71e2be923f449ba9267c4458e2500b2024-01-24T05:17:10ZengElsevierAlexandria Engineering Journal1110-01682024-01-0186425442Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate conditionMasoud Karbasi0Mumtaz Ali1Sayed M. Bateni2Changhyun Jun3Mehdi Jamei4Zaher Mundher Yaseen5Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, Canada; Water Engineering Department, Faculty of Agriculture, University of Zanjan, Zanjan, Iran; Corresponding author at: Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, Canada.UniSQ College, University of Southern Queensland, QLD 4305, Australia; Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A4P3, CanadaDept. of Civil and Environmental Engineering and Water Resources Research Center, Univ. of Hawaii at Manoa, 2540 Dole St., Holmes 342, Honolulu, HI 96822, USADepartment of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Seoul, South Korea; Corresponding author.Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters, PE, Canada; Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi‑Qar, Nasiriyah 64001, IraqCivil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaIn this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and long short-term memory (LSTM), were used along with adaptive boosting and general regression neural network to forecast multi-step-ahead pan evaporation in two arid climate stations in Iran (Ahvaz and Yazd). Lagged time series of meteorological data and pan evaporation data were input to the machine learning models. Two feature selection methods, i.e., the Boruta extra tree and XGBoost, were used to select significant inputs to reduce the number of inputs and model complexity. Different statistical metrics were used to investigate the model performance. The results demonstrated that Boruta-extra-tree-based models were more accurate than XGBoost-based models. Compared with the machine learning techniques, the combination of Boruta extra tree and BiLSTM enabled more accurate one-day-ahead forecasting of pan evaporation for both sites (Root Mean Square Error (RMSE) = 1.6857, for the Ahvaz station, and RMSE = 1.3996 for the Yazd station). The proposed model was used to forecast up to 30 days ahead of pan evaporation in both stations. The results showed that the Boruta-BiLSTM model could accurately forecast the pan evaporation for up to 30 days in both stations.http://www.sciencedirect.com/science/article/pii/S1110016823010499Pan EvaporationForecastingMachine LearningDeep LearningBoruta Feature Selection |
| spellingShingle | Masoud Karbasi Mumtaz Ali Sayed M. Bateni Changhyun Jun Mehdi Jamei Zaher Mundher Yaseen Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition Alexandria Engineering Journal Pan Evaporation Forecasting Machine Learning Deep Learning Boruta Feature Selection |
| title | Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition |
| title_full | Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition |
| title_fullStr | Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition |
| title_full_unstemmed | Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition |
| title_short | Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition |
| title_sort | boruta extra tree bidirectional long short term memory model development for pan evaporation forecasting investigation of arid climate condition |
| topic | Pan Evaporation Forecasting Machine Learning Deep Learning Boruta Feature Selection |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823010499 |
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