Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale

Application in Daily Streamflow Simulation

Document identifier: oai:DiVA.org:ltu-77876
Access full text here:10.1109/ACCESS.2020.2974406
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Deep learning model, Streamow forecasting, Tropical environment, Window scale forecasting, LSTM, Soil Mechanics
Publication year: 2020
Abstract:

Streamow forecasting is essential for hydrological engineering. In accordance with theadvancement of computer aids in this eld, various machine learning (ML) models have been explored tosolve this highly non-stationary, stochastic, and nonlinear problem. In the current research, a newly exploredversion of an ML model called the long short-term memory (LSTM) was investigated for streamowprediction using historical data for forecasting for a particular period. For a case study located in a tropicalenvironment, the Kelantan river in the northeast region of the Malaysia Peninsula was selected. Themodelling was performed according to several perspectives: (i) The feasibility of applying the developedLSTM model to streamow prediction was veried, and the performance of the developed LSTM modelwas compared with the classic backpropagation neural network model; (ii) In the experimental process ofapplying the LSTM model to the prediction of streamow, the inuence of the training set size on theperformance of the developed LSTM model was tested; (iii) The effect of the time interval between thetraining set and the testing set on the performance of the developed LSTM model was tested; (iv) The effectof the time span of the prediction data on the performance of the developed LSTM model was tested. Theexperimental data showthat not only does the developedLSTM model have obvious advantages in processingsteady streamow data in the dry season but it also shows good ability to capture data features in the rapidlyuctuant streamow data in the rainy season.

Authors

Minglei Fu

College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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Tingchao Fan

College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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Zi’ang Ding

College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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Sinan Q. Salih

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
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Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
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Zaher Mundher Yaseen

Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietna
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