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

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• 作者：Ravinesh C Deo ; Mehmet Şahin
• 关键词：Extreme learning machine ; Streamflow prediction ; Hydrological modeling
• 刊名：Environmental Monitoring and Assessment
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：188
• 期：2
• 全文大小：1,759 KB
• 参考文献：Abbot, J., & Marohasy, J. (2012). Application of artificial neural networks to rainfall forecasting in Queensland. Australia Advances in Atmospheric Sciences, 29, 717–730.CrossRef
  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 .
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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 .CrossRef
  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.CrossRef
  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.CrossRef
  Fox, D. G. (1981). Judging air quality model performance. Bulletin of the American Meteorological Society, 62, 599–609.CrossRef
  Haykin, S. (2010). Neural networks: a comprehensive foundation, 1994 Mc Millan. New: Jersey.
  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.CrossRef
  Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70, 489–501.CrossRef
  Huang, G., Huang, G.-B., Song, S., & You, K. (2015). Trends in extreme learning machines. A review Neural Networks, 61, 32–48.CrossRef
  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.CrossRef
  Kiem, A. S., & Franks, S. W. (2004). Multi-decadal variability of drought risk, eastern Australia. Hydrological Processes, 18, 2039–2050.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  Nash, J., & Sutcliffe, J. (1970). River flow forecasting through conceptual models part I—a discussion of principles. Journal of Hydrology, 10, 282–290.CrossRef
  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.CrossRef
  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.CrossRef
  Nicholls, N., Drosdowsky, W., & Lavery, B. (1997). Australian rainfall variability and change. Weather, 52, 66–72.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  Saji, N., Goswami, B. N., Vinayachandran, P., & Yamagata, T. (1999). A dipole mode in the tropical Indian Ocean. Nature, 401, 360–363.
  Saji, N., & Yamagata, T. (2003). Possible impacts of Indian ocean dipole mode events on global climate. Climate Research, 25, 151–169.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  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.CrossRef
  Taschetto, A. S., & England, M. H. (2009). El Niño Modoki impacts on Australian rainfall. Journal of Climate, 22, 3167–3174.CrossRef
  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.CrossRef
  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.CrossRef
  Ulgen, K., & Hepbasli, A. (2002). Comparison of solar radiation correlations for Izmir. Turkey International Journal of Energy Research, 26, 413–430.CrossRef
  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.CrossRef
  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.
  Willmott, C. J. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63, 1309–1313.CrossRef
  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.CrossRef
  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.CrossRef
  
• 作者单位：Ravinesh C Deo (1)
  Mehmet Şahin (2)
  
  1. School of Agricultural Computational and Environmental Sciences, International Centre of Applied Climate Science (ICACS), University of Southern Queensland, Springfield, QLD, 4300, Australia
  2. Department of Electrical and Electronics Engineering, Siirt University, 56100, Siirt, Turkey
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Monitoring, Environmental Analysis and Environmental Ecotoxicology
  Ecology
  Atmospheric Protection, Air Quality Control and Air Pollution
  Environmental Management
  
• 出版者：Springer Netherlands
• ISSN：1573-2959

文摘

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.