Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq

Yaseen, Zaher Mundher and Jaafar, Othman and Deo, Ravinesh C. and Kisi, Ozgur and Adamowski, Jan and Quilty, John and El-Shafie, Ahmed (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. Journal of Hydrology, 542. pp. 603-614. ISSN 0022-1694

Abstract

Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, later use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott’s Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model’s effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS= 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Dr RC Deo was supported by Academic Division Researcher Activation Incentive Scheme (RAIS; July– September 2015) grant and Australian Government Endeavor Executive Fellowship (2015). Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 22 Sep 2016 02:32
Last Modified: 24 Jan 2018 02:03
Uncontrolled Keywords: extreme learning machine; Stream-flow forecasting; Support vector regression; Generalized regression neural network; Semi-arid; Iraq
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040104 Climate Change Processes
05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040607 Surface Processes
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
01 Mathematical Sciences > 0102 Applied Mathematics > 010207 Theoretical and Applied Mechanics
09 Engineering > 0905 Civil Engineering > 090509 Water Resources Engineering
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
D Environment > 96 Environment > 9610 Natural Hazards > 961099 Natural Hazards not elsewhere classified
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960203 Weather
Funding Details:
Identification Number or DOI: 10.1016/j.jhydrol.2016.09.035
URI: http://eprints.usq.edu.au/id/eprint/29744

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