Newly explored machine learning model for river flow time series forecasting at Mary River, Australia
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Newly explored machine learning model for river flow time series forecasting at Mary River, Australia Fang Cui & Sinan Q. Salih & Bahram Choubin & Suraj Kumar Bhagat & Pijush Samui & Zaher Mundher Yaseen
Received: 20 May 2020 / Accepted: 3 November 2020 # Springer Nature Switzerland AG 2020
Abstract Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel dataintelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011–2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were
validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the
F. Cui Key Lab of Disasters Monitoring and Mechanism Simulating of Shannxi Province, Baoji University of Art & Sciences, Baoji 721013 Shannxi, People’s Republic of China e-mail: [email protected]
B. Choubin Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran e-mail: [email protected]
F. Cui Geography and Environment Department, Baoji University of Art & Sciences, Baoji 721013 Shannxi, People’s Republic of China
S. K. Bhagat Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam e-mail: [email protected]
S. Q. Salih Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam e-mail: [email protected]
P. Samui Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India e-mail: [email protected]
S. Q. Salih Computer Science Department, Dijlah University College, Baghdad, Iraq
Z. M. Yaseen (*) Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam e-mail: [email protected]
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proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to
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