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Hybrid river stage ...
Hybrid river stage forecasting based on machine learning with empirical mode decomposition
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- Heddam, Salim (författare)
- Faculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria
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- Vishwakarma, Dinesh Kumar (författare)
- Department of Irrigation and Drainage Engineering, Govind Ballabh Pant University of Agriculture and Technology, 263145, Pantnagar, Uttarakhand, India
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- Abed, Salwan Ali (författare)
- Department of Environment, College of Science, University of Al-Qadisiyah, 58001, Al-Qadisiyah, Iraq
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- Sharma, Pankaj (författare)
- Department of Soil and Water Engineering, Punjab Agricultural University, 141027, Ludhiana, Punjab, India
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- Al-Ansari, Nadhir (författare)
- Luleå tekniska universitet,Geoteknologi
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- Alataway, Abed (författare)
- Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, 11451, Riyadh, Saudi Arabia
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- Dewidar, Ahmed Z. (författare)
- Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, 11451, Riyadh, Saudi Arabia; Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, 11451, Riyadh, Saudi Arabia
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- Mattar, Mohamed A. (författare)
- Prince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud University, 11451, Riyadh, Saudi Arabia; Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, 11451, Riyadh, Saudi Arabia; Agricultural Engineering Research Institute (AEnRI), Agricultural Research Centre, 12618, Giza, Egypt
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(creator_code:org_t)
- Springer Nature, 2024
- 2024
- Engelska.
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Ingår i: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:3
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few decades, the use of machine learning paradigm to investigate complex hydrological systems has gained significant importance, and forecasting river stage is one of the promising areas of investigations. Traditional in situ measurements, which are sometime restricted by the existing of several handicaps especially in terms of regular access to any points alongside the streams and rivers, can be overpassed by the use of modeling approaches. For more accurate forecasting of river stages, we suggest a new modeling framework based on machine learning. A hybrid forecasting approach was developed by combining machine learning techniques, namely random forest regression (RFR), bootstrap aggregating (Bagging), adaptive boosting (AdaBoost), and artificial neural network (ANN), with empirical mode decomposition (EMD) to provide a robust forecasting model. The singles models were first applied using only the river stage data without preprocessing, and in the following step, the data were decomposed into several intrinsic mode functions (IMF), which were then used as new input variables. According to the obtained results, the proposed models showed improved results compared to the standard RFR without EMD for which, the error performances metrics were drastically reduced, and the correlation index was increased remarkably and great changes in models’ performances have taken place. The RFR_EMD, Bagging_EMD, and AdaBoost_EMD were less accurate than the ANN_EMD model, which had higher R≈0.974, NSE≈0.949, RMSE≈0.330 and MAE≈0.175 values. While the RFR_EMD and the Bagging_EMD were relatively equal and exhibited the same accuracies higher than the AdaBoost_EMD, the superiority of the ANN_EMD was obvious. The proposed model shows the potential for combining signal decomposition with machine learning, which can serve as a basis for new insights into river stage forecasting.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Geotechnical Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
Nyckelord
- ANN
- Bagging
- Boosting
- Forecasting
- Lag time
- RFR
- River
- Stage
- Geoteknik
- Soil Mechanics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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