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

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An Erratum to this article was published on 07 March 2016

Abstract

A predictive model for streamflow has practical implications for understanding the drought hydrology, environmental monitoring and agriculture, ecosystems and resource management. In this study, the state-or-art extreme learning machine (ELM) model was utilized to simulate the mean streamflow water level (Q WL) for three hydrological sites in eastern Queensland (Gowrie Creek, Albert, and Mary River). The performance of the ELM model was benchmarked with the artificial neural network (ANN) model. The ELM model was a fast computational method using single-layer feedforward neural networks and randomly determined hidden neurons that learns the historical patterns embedded in the input variables. A set of nine predictors with the month (to consider the seasonality of Q WL); rainfall; Southern Oscillation Index; Pacific Decadal Oscillation Index; ENSO Modoki Index; Indian Ocean Dipole Index; and Nino 3.0, Nino 3.4, and Nino 4.0 sea surface temperatures (SSTs) were utilized. A selection of variables was performed using cross correlation with Q WL, yielding the best inputs defined by (month; P; Nino 3.0 SST; Nino 4.0 SST; Southern Oscillation Index (SOI); ENSO Modoki Index (EMI)) for Gowrie Creek, (month; P; SOI; Pacific Decadal Oscillation (PDO); Indian Ocean Dipole (IOD); EMI) for Albert River, and by (month; P; Nino 3.4 SST; Nino 4.0 SST; SOI; EMI) for Mary River site. A three-layer neuronal structure trialed with activation equations defined by sigmoid, logarithmic, tangent sigmoid, sine, hardlim, triangular, and radial basis was utilized, resulting in optimum ELM model with hard-limit function and architecture 6-106-1 (Gowrie Creek), 6-74-1 (Albert River), and 6-146-1 (Mary River). The alternative ELM and ANN models with two inputs (month and rainfall) and the ELM model with all nine inputs were also developed. The performance was evaluated using the mean absolute error (MAE), coefficient of determination (r 2), Willmott’s Index (d), peak deviation (P dv), and Nash–Sutcliffe coefficient (E NS). The results verified that the ELM model as more accurate than the ANN model. Inputting the best input variables improved the performance of both models where optimum ELM yielded R 2 ≈ (0.964, 0.957, and 0.997), d ≈ (0.968, 0.982, and 0.986), and MAE ≈ (0.053, 0.023, and 0.079) for Gowrie Creek, Albert River, and Mary River, respectively, and optimum ANN model yielded smaller R 2 and d and larger simulation errors. When all inputs were utilized, simulations were consistently worse with R 2 (0.732, 0.859, and 0.932 (Gowrie Creek), d (0.802, 0.876, and 0.903 (Albert River), and MAE (0.144, 0.049, and 0.222 (Mary River) although they were relatively better than using the month and rainfall as inputs. Also, with the best input combinations, the frequency of simulation errors fell in the smallest error bracket. Therefore, it can be ascertained that the ELM model offered an efficient approach for the streamflow simulation and, therefore, can be explored for its practicality in hydrological modeling.

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Abbreviations

ANNs:

Artificial neural networks

BOM:

Bureau of Meteorology

DERM:

Department of Environment

D :

Willmott’s Index of Agreement

E :

Nash–Sutcliffe coefficient

EMI:

ENSO Modoki Index

ELM:

Extreme learning machine

G tan sig :

Tangent sigmoid function

G sin :

Sine activation function

G hard-lim :

Hard-limit activation function

G rad bas :

Radial basis activation function

G tri bas :

Triangular basis function

G log sig :

Logarithmic sigmoid activation function

IOD:

Indian Ocean Dipole

JISAO:

Joint Institute of the Study of the Atmosphere and Ocean

P :

Precipitation (or rainfall)

PE:

Prediction error

PDO:

Pacific Decadal Oscillation

Q WL pred :

Simulated streamflow water level

Q WL obs :

Observed streamflow water level

MAE:

Mean absolute error

R 2 :

Coefficient of determination

SLFN:

Single-layer feedforward neural network

SOI:

Southern Oscillation Index

SST:

Sea surface temperature

References

  • Abbot, J., & Marohasy, J. (2012). Application of artificial neural networks to rainfall forecasting in Queensland. Australia Advances in Atmospheric Sciences, 29, 717–730.

    Article  Google Scholar 

  • Abbot, J., & Marohasy, J. (2014). Input selection and optimisation for monthly rainfall forecasting in Queensland. Australia, using artificial neural networks Atmospheric Research, 138, 166–178. doi:10.1016/j.atmosres.2013.11.002.

    Google Scholar 

  • Acharya N, Shrivastava NA, Panigrahi B, Mohanty U (2013) Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine Climate Dynamics:1–8

  • Adamowski J, Fung Chan H, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada Water Resources Research 48

  • Adamowski, J. F. (2008). Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. Journal of Hydrology, 353, 247–266.

    Article  Google Scholar 

  • Asefa, T., Kemblowski, M., McKee, M., & Khalil, A. (2006). Multi-time scale stream flow predictions: the support vector machines approach. Journal of Hydrology, 318, 7–16.

    Article  Google Scholar 

  • Ashok, K., Guan, Z., Yamagata, T. (2003). Influence of the Indian ocean dipole on the Australian winter rainfall. Geophysical Research Letters, 30. doi:10.1029/2003GL017926.

  • Ashok, K., Behera, S. K., Rao, S. A., Weng, H., Yamagata, T. (2007). El Niño Modoki and its possible teleconnection. Journal of Geophysical Research: Oceans, (1978–2012) 112. doi:10.1029/2006JC003798.

  • Belayneh A, Adamowski J (2012) Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression Applied Computational Intelligence and Soft Computing 2012:6 doi:10.1155/2012/794061.

  • Brodie, R. S., Hostetler, S., & Slatter, E. (2008). Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity. Journal of hydrology, 349, 56–67.

    Article  Google Scholar 

  • Cai W, Cowan T (2009) La Niña Modoki impacts Australia autumn rainfall variability Geophysical Research Letters 36

  • Chang, F., Chang, L. C., & Huang, H. L. (2002). Real time recurrent learning neural network for stream flow forecasting. Hydrological Processes, 16, 2577–2588.

    Article  Google Scholar 

  • Chowdhury R, Gardner T, Gardiner R, Chong M, Tonks M, Begbie D, Wakem S Catchment hydrology modelling for stormwater harvesting study in SEQ: from instrumentation to simulation. In: Science Forum, 2010

  • Chiew, F. H., & McMahon, T. A. (2002). Modelling the impacts of climate change on Australian streamflow. Hydrological Processes, 16, 1235–1245.

    Article  Google Scholar 

  • Chiew, F. H., Piechota, T. C., Dracup, J. A., & McMahon, T. A. (1998). El Nino/Southern Oscillation and Australian rainfall, streamflow and drought: links and potential for forecasting. Journal of Hydrology, 204, 138–149.

    Article  Google Scholar 

  • Deo, R. C., & Şahin, M. (2015a). Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern. Australia Atmospheric Research, 161–162, 65–81.

    Article  Google Scholar 

  • Deo, R. C., & Şahin, M. (2015b). Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern. Australia Atmospheric Research, 153, 512–525. doi:10.1016/j.atmosres.2013.11.002.

    Article  Google Scholar 

  • Deo RC, Samui P, Kim D (2015) Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models Stochastic Environmental Research and Risk Assessment:1–16

  • Deo RC, Syktus J, McAlpine C, Lawrence P, McGowan H, Phinn SR (2009) Impact of historical land cover change on daily indices of climate extremes including droughts in eastern Australia Geophysical Research Letters 36

  • Dettinger, M. D., & Diaz, H. F. (2000). Global characteristics of stream flow seasonality and variability. Journal of Hydrometeorology, 1, 289–310.

    Article  Google Scholar 

  • Dettinger, M. D., Cayan, D. R., McCabe, G. J., Marengo, J. A. (2000). Multiscale streamflow variability associated with El Nino/Southern oscillation. Cambridge: Cambridge University Press.

  • DNRM (2014) Establishing a new water monitoring site (WM65). version 1.0, Brisbane Qld: State of Queensland (Department of Natural Resources and Mines), Service Delivery.

  • Drosdowsky, W. (1993). An analysis of Australian seasonal rainfall anomalies: 1950–1987. II: temporal variability and teleconnection patterns. International Journal of Climatology, 13, 111–149.

    Article  Google Scholar 

  • Fox, D. G. (1981). Judging air quality model performance. Bulletin of the American Meteorological Society, 62, 599–609.

    Article  Google Scholar 

  • Haykin, S. (2010). Neural networks: a comprehensive foundation, 1994 Mc Millan. New: Jersey.

    Google Scholar 

  • Haylock, M., & Nicholls, N. (2000). Trends in extreme rainfall indices for an updated high quality data set for Australia, 1910–1998. International Journal of Climatology, 20, 1533–1541.

  • Hennessy K et al. (2007) Australia and New Zealand in ML Parry, OF Canziana, JP Palitikof, PJ van der Linder, and CE Hanson, editors. Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  • Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press

  • Huang, G.-B., & Chen, L. (2007). Convex incremental extreme learning machine. Neurocomputing, 70, 3056–3062.

    Article  Google Scholar 

  • Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70, 489–501.

    Article  Google Scholar 

  • Huang, G., Huang, G.-B., Song, S., & You, K. (2015). Trends in extreme learning machines. A review Neural Networks, 61, 32–48.

    Article  Google Scholar 

  • IPCC (2001) The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change In: Houghton JT, Ding, Y., Griggs, D. J., Noguer, M., Van der Linden, P. J., Dai, X., Maskell, K. and Johnson, C. A. (Eds.) (ed). Cambridge University Press, Cambridge and New York

  • Kiem, A. S., & Franks, S. W. (2001). On the identification of ENSO-induced rainfall and runoff variability. A Comparison of Methods and Indices Hydrological Sciences Journal, 46, 715–727.

    Article  Google Scholar 

  • Kiem, A. S., & Franks, S. W. (2004). Multi-decadal variability of drought risk, eastern Australia. Hydrological Processes, 18, 2039–2050.

    Article  Google Scholar 

  • Kiem AS, Franks SW, Kuczera G (2003) Multi-decadal variability of flood risk Geophysical Research Letters 30

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection vol 1. MIT press

  • Krause, P., Boyle, D., & Bäse, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89–97.

    Article  Google Scholar 

  • Legates, D. R., & McCabe, G. J. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35, 233–241.

    Article  Google Scholar 

  • Leu S-S, Adi TJW (2011) Probabilistic prediction of tunnel geology using a Hybrid Neural-HMM Engineering Applications of Artificial Intelligence 24:658–665

  • Lippman R (1987) An introduction to computing with neural nets IEEE ASSP Magazine 4:4–22

  • Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Environmental modelling & software 15:101–124

  • Maier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental Modelling & Software, 25, 891–909.

    Article  Google Scholar 

  • Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A Pacific interdecadal climate oscillation with impacts on salmon production Bulletin of the American Meteorological Society 78:1069–1079

  • Masinde M (2013) Artificial neural networks models for predicting effective drought index: factoring effects of rainfall variability Mitigation and Adaptation Strategies for Global Change:1–24

  • McAlpine C, Syktus J, Deo R, Lawrence P, McGowan H, Watterson I, Phinn S (2007) Modeling the impact of historical land cover change on Australia’s regional climate Geophysical Research Letters 34

  • McAlpine, C., Syktus, J., Ryan, J., Deo, R., McKeon, G., McGowan, H., & Phinn, S. (2009). A continent under stress: interactions, feedbacks and risks associated with impact of modified land cover on Australia’s climate. Global Change Biology, 15, 2206–2223.

    Article  Google Scholar 

  • McBride JL, Nicholls N (1983) Seasonal relationships between Australian rainfall and the Southern Oscillation Monthly Weather Review 111:1998–2004

  • McMahon TA, Finlayson B, Haines A, Srikanthan R (1992) Global runoff: continental comparisons of annual flows and peak discharges. Catena Verlag

  • Mekanik, F., Imteaz, M., Gato-Trinidad, S., & Elmahdi, A. (2013). Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. Journal of Hydrology, 503, 11–21.

    Article  Google Scholar 

  • Morid, S., Smakhtin, V., & Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology, 27, 2103–2111.

    Article  Google Scholar 

  • Nash, J., & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I—a discussion of principles. Journal of Hydrology, 10, 282–290.

    Article  Google Scholar 

  • Nastos, P., Paliatsos, A., Koukouletsos, K., Larissi, I., & Moustris, K. (2014). Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens. Greece Atmospheric Research, 144, 141–150.

    Article  Google Scholar 

  • Ni, Q., Wang, L., Ye, R., Yang, F., & Sivakumar, M. (2010). Evolutionary modeling for streamflow forecasting with minimal datasets: a case study in the West Malian River. China Environmental Engineering Science, 27, 377–385.

    Article  CAS  Google Scholar 

  • Nicholls, N., Drosdowsky, W., & Lavery, B. (1997). Australian rainfall variability and change. Weather, 52, 66–72.

    Article  Google Scholar 

  • Ortiz-García, E., Salcedo-Sanz, S., & Casanova-Mateo, C. (2014). Accurate precipitation prediction with support vector classifiers: a study including novel predictive variables and observational data. Atmospheric Research, 139, 128–136.

    Article  Google Scholar 

  • Ortiz-García, E., Salcedo-Sanz, S., Casanova-Mateo, C., Paniagua-Tineo, A., & Portilla-Figueras, J. (2012). Accurate local very short-term temperature prediction based on synoptic situation Support Vector Regression banks. Atmospheric Research, 107, 1–8.

    Article  Google Scholar 

  • Ouyang, R., Liu, W., Fu, G., Liu, C., Hu, L., & Wang, H. (2014). Linkages between ENSO/PDO signals and precipitation, streamflow in China during the last 100 years. Hydrology and Earth System Sciences, 18, 3651–3661.

    Article  Google Scholar 

  • Patterson DW (1998) Artificial neural networks: theory and applications. Prentice Hall PTR

  • Paulescu, M., Tulcan‐Paulescu, E., & Stefu, N. (2011). A temperature based model for global solar irradiance and its application to estimate daily irradiation values. International Journal of Energy Research, 35, 520–529.

    Article  Google Scholar 

  • Piechota, T. C., Chiew, F. H., Dracup, J. A., & McMahon, T. A. (2001). Development of exceedance probability streamflow forecast. Journal of Hydrologic Engineering, 6, 20–28.

    Article  Google Scholar 

  • Power, S., Casey, T., Folland, C., Colman, A., & Mehta, V. (1999). Inter-decadal modulation of the impact of ENSO on Australia. Climate Dynamics, 15, 319–324.

    Article  Google Scholar 

  • Rajesh, R., & Prakash, J. S. (2011). Extreme learning machines—a review and state-of-the-art. International Journal of Wisdom Based Computing, 1, 35–49.

    Google Scholar 

  • Risbey, J. S., Pook, M. J., McIntosh, P. C., Wheeler, M. C., & Hendon, H. H. (2009). On the remote drivers of rainfall variability in Australia. Monthly Weather Review, 137, 3233–3253.

    Article  Google Scholar 

  • Robertson D, Wang Q (2008) An investigation into the selection of predictors and skill assessment using the Bayesian joint probability (BJP) modelling approach to seasonal forecasting of streamflows Water for a Healthy Country flagship report, CSIRO Land and Water, Canberra

  • Şahin, M. (2012). Modelling of air temperature using remote sensing and artificial neural network in Turkey. Advances in Space Research, 50, 973–985.

    Article  Google Scholar 

  • Şahin, M., Kaya, Y., & Uyar, M. (2013). Comparison of ANN and MLR models for estimating solar radiation in turkey using NOAA/AVHRR data. Advances in Space Research, 51, 891–904.

    Article  Google Scholar 

  • Şahin, M., Kaya, Y., Uyar, M., & Yıldırım, S. (2014). Application of extreme learning machine for estimating solar radiation from satellite data. International Journal of Energy Research, 38, 205–212.

    Article  Google Scholar 

  • Saji, N., Goswami, B. N., Vinayachandran, P., & Yamagata, T. (1999). A dipole mode in the tropical Indian Ocean. Nature, 401, 360–363.

    CAS  Google Scholar 

  • Saji, N., & Yamagata, T. (2003). Possible impacts of Indian ocean dipole mode events on global climate. Climate Research, 25, 151–169.

    Article  Google Scholar 

  • Salcedo-Sanz S, Deo RC, Carro-Calvo L, Saavedra-Moreno B (2015) Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms Theoretical and Applied Climatology: DOI: 10.1007/s00704-00015-01480-00704 doi:10.1007/s00704-015-1480-4

  • Salcedo-Sanz, S., Pastor-Sánchez, A., Prieto, L., Blanco-Aguilera, A., & García-Herrera, R. (2014). Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization—extreme learning machine approach. Energy Conversion and Management, 87, 10–18.

    Article  Google Scholar 

  • Samui, P., & Dixon, B. (2012). Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs. Hydrological Processes, 26, 1361–1369.

    Article  Google Scholar 

  • Sánchez-Monedero, J., Salcedo-Sanz, S., Gutiérrez, P., Casanova-Mateo, C., & Hervás-Martínez, C. (2014). Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal–ordinal support vector classifier. Engineering Applications of Artificial Intelligence, 34, 199–207.

    Article  Google Scholar 

  • Shukla, R. P., Tripathi, K. C., Pandey, A. C., & Das, I. (2011). Prediction of Indian summer monsoon rainfall using Niño indices: a neural network approach. Atmospheric Research, 102, 99–109.

    Article  Google Scholar 

  • Simpson, H., Cane, M., Herczeg, A., Zebiak, S., & Simpson, J. (1993). Annual river discharge in southeastern Australia related to El Nino-Southern Oscillation forecasts of sea surface temperatures. Water Resources Research, 29, 3671–3680.

    Article  Google Scholar 

  • Taschetto, A. S., & England, M. H. (2009). El Niño Modoki impacts on Australian rainfall. Journal of Climate, 22, 3167–3174.

    Article  Google Scholar 

  • Tiwari, M. K., & Adamowski, J. (2013). Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resources Research, 49, 6486–6507.

    Article  Google Scholar 

  • Tran H, Muttil N, Perera B Investigation of artificial neural network models for streamflow forecasting. In: 19th International Congress on Modelling and Simulation (MODSIM2011), 2011. Modelling and Simulation Society of Australia and New Zealand Inc.(MSSANZ), pp 1099–1105

  • Trenberth, K. E. (1984). Signal versus noise in the Southern Oscillation. Monthly Weather Review, 112, 326–332.

    Article  Google Scholar 

  • Ulgen, K., & Hepbasli, A. (2002). Comparison of solar radiation correlations for Izmir. Turkey International Journal of Energy Research, 26, 413–430.

    Article  Google Scholar 

  • Verdon DC, Wyatt AM, Kiem AS, Franks SW (2004) Multidecadal variability of rainfall and streamflow: Eastern Australia Water Resources Research 40

  • Vogl, T., Mangis, J., Rigler, A., Zink, W., & Alkon, D. (1988). Accelerating the convergence of the backpropagation method. Biological Cybernetics, 59, 257–263.

    Article  Google Scholar 

  • Wang E, Zhang Y, Luo J, Chiew F, Wang Q (2011) Monthly and seasonal streamflow forecasts using rainfall runoff modeling and historical weather data Water Resources Research 47

  • Wang Q, Robertson D (2011) Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences Water Resources Research 47

  • Wang Q, Robertson D, Chiew F (2009) A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites Water Resources Research 45

  • Weng, H., Ashok, K., Behera, S. K., Rao, S. A., Yamagata, T. (2007). Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific rim during boreal summer. Climate Dynamics, 29, 113–129.

  • Willmott, C. J. (1981). On the validation of models. Physical Geography, 2, 184–194.

    Google Scholar 

  • Willmott, C. J. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63, 1309–1313.

    Article  Google Scholar 

  • Willmott CJ (1984) On the evaluation of model performance in physical geography. In: Spatial statistics and models. Springer, pp 443–460

  • Wu, C., & Chau, K. (2010). Data-driven models for monthly streamflow time series prediction. Engineering Applications of Artificial Intelligence, 23, 1350–1367.

    Article  Google Scholar 

  • Zhang Y, Wallace JM, Battisti DS (1997) ENSO-like interdecadal variability: 1900–93 Journal of climate 10:1004–1020

  • Zubair, L., & Chandimala, J. (2006). Epochal changes in ENSO-streamflow relationships in Sri Lanka. Journal of hydrometeorology, 7, 1237–1246.

    Article  Google Scholar 

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Acknowledgments

The data were acquired from the Australian Bureau of Meteorology, Joint Institute of the Study of Atmosphere and Ocean (JISAO), and Japanese Agency for Marine-Earth Science and Technology. Dr RC Deo was supported by the Academic Division Researcher Activation Incentive Scheme (RAIS; July–September 2015) grant and the Australian Government Endeavor Executive Fellowship (2015) to Dr R.C. Deo to collaborate with Dr M Sahin (Turkey). We thank two reviewers and the Editor for their comments that improved the overall clarity of this paper.

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  1. School of Agricultural Computational and Environmental Sciences, International Centre of Applied Climate Science (ICACS), University of Southern Queensland, Springfield, QLD, 4300, Australia

    Ravinesh C Deo

  2. Department of Electrical and Electronics Engineering, Siirt University, 56100, Siirt, Turkey

    Mehmet Şahin

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Deo, R.C., Şahin, M. An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environ Monit Assess 188, 90 (2016). https://doi.org/10.1007/s10661-016-5094-9

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