An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland

Deo, Ravinesh C. and Sahin, Mehmet (2016) An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environmental Monitoring and Assessment, 188 (90). ISSN 0167-6369

Abstract

A predictive model for streamflow has practical implications for understanding drought hydrology, environmental monitoring and agriculture, ecosystem and resource management. In this study state-or-art Extreme Learning Machine (ELM) model was utilized to simulate the mean streamflow water level (QWL) for three hydrological sites in eastern Queensland (Gowrie Creek, Albert River & Mary River). The performance of ELM model was benchmarked with the Artificial Neural Network (ANN) model. The ELM model was a fast three step simulation method designed using the Single Layer Feedforward Neural Network (SLFNs) and randomly determined hidden neurons that learned the historical patterns embedded in the input variables related to QWL. A set of nine predictors with the month (to consider seasonality of QWL), monthly rainfall, Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO) Index, ENSO Modoki Index (EMI), Indian Ocean Dipole (IOD) Index and Nino 3.0SST, Nino 3.4SST and Nino 4.0SSTs were utilized. A pre-selection of variables was performed using cross correlation analysis with QWL, yielding the best inputs defined by (month; P; Nino 3.0SST; Nino4.0SST; SOI; EMI) for Gowrie Creek, (month; P; SOI; PDO; IOD; EMI) for Albert River and (month; P; Nino3.4 SST; Nino4.0 SST; SOI; EMI) for Mary River site. A three layer neuronal structure was employed to trial activation functions defined by the sigmoid, logarithmic, tangent sigmoid, sine, hardlim and triangular and radial basis equations for feature extraction that utilized between 2 to 150 hidden neurons. This resulted in the optimum ELM model executed by hard-limit function with a neuronal architecture 6-106-1 (Gowrie Creek), 6-74-1 (Albert River) and 6-146-1 (Mary River). The simulations were also performed with two inputs (month & rainfall) and all nine inputs. The model performance was evaluated using mean absolute error (MAE), coefficient of determination (r2), Willmott index (d), peak percentage deviation (Pdv) and Nash-Sutcliffe coefficient (ENS). Results found that the ELM was more accurate than the ANN model for simulation of QWL. Inputting the best six input variables improved the performance of both models where the optimum model ELM yielded R2  (0.964, 0.957 & 0.997), d  (0.968, 0.982 & 0.986), MAE  (0.053, 0.023 & 0.079 for Gowrie Creek, Albert River and Mary River, respectively, and the optimum ANN model yielded smaller R2 and d and larger simulation errors. When all nine inputs were utilised, the simulations were consistently worse for all stations with R2 (0.732, 0.859 & 0.932 (Gowrie Creek); d (0.802, 0.876 & 0.903 (Albert River) and MAE (0.144, 0.049 & 0.222 (Mary River) although they were relatively better than using only the month and rainfall as inputs. Also, with best input combinations, the frequency of simulation errors fell in smallest error bracket. Therefore, it is ascertained that the ELM algorithm offers an efficient soft-computing approach for simulation of streamflow, and therefore, can be explored further for its practicality in hydrological modeling.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher. Dr RC Deo was supported by Academic Division Researcher Activation Incentive Scheme (RAIS; July– September 2015) grant and Australian Government Endeavor Executive Fellowship (2015)to collaborate with Dr M Sahin (Turkey).
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 18 Jan 2016 23:52
Last Modified: 22 Sep 2016 07:08
Uncontrolled Keywords: extreme learning machine; streamflow prediction; hydrological modeling
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: 10.1007/s10661-016-5094-9
URI: http://eprints.usq.edu.au/id/eprint/28347

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