Abstract
Streamflow estimation plays a significant role in water resources management, especially for flood mitigation, drought warning, and reservoir operation. Hence, the current study examines the prediction capability of three well-known machine learning algorithms (Support Vector Regression (SVR), Artificial Neural Network with backpropagation (ANN-BP), and Extreme Learning Machine (ELM)) for the monthly and daily streamflows of four rivers in the United States. For model development, three main predictor variables (P, Tmax, and Tmin) and their antecedent values were considered. The SVM-RFE feature selection method was used to select the most appropriate predictor variable.The performance of the developed models was tested using four evaluation statistics. The results indicate that (1) except some improvements, the accuracy of all models decreases at the daily scale compared to that at the monthly scale; (2) the SVR has the best performance among the three models at the monthly and daily scales, while the ANN-BP model has the worse performance; (3) the ELM has better generalization performance than the ANN-BP for streamflow simulation at the monthly and daily scales; and (4) all models fail to predict the streamflow for the Carson River as a snowmelt-dominated basin. Generally, findings of the current study indicate that the SVR model produces better results than the ELM and ANN-BP for streamflow simulation at the monthly and daily scales.
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Adnan RM, Liang Z, Trajkovic S, Zounemat-Kermani M, Li B, Kisi O (2019) Daily streamflow prediction using optimally pruned extreme learning machine. J Hydrol 577:123981
Atiquzzaman M, Kandasamy J (2016) Prediction of hydrological time-series using extreme learning machine. J Hydroinf 18(2):345–353
Barzegar R, Moghaddam AA, Adamowski J, Fijani E (2017) Comparison of machine learning models for predicting fluoride contamination in groundwater. Stoch Env Res Risk A 31(10):2705–2718
Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the Awash River basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J Hydrol 508:418–429
Benke AC, Cushing CE (2011) Rivers of North America. Elsevier, Amsterdam
Bonada N, Resh VH (2013) Mediterranean-climate streams and rivers: geographically separated but ecologically comparable freshwater systems. Hydrobiologia 719(1):1–29
Campolo M, Soldati A, Andreussi P (2003) Artificial neural network approach to flood forecasting in the river Arno. Hydrol Sci J 48(3):381–398
Cheng C-T, Zhao M-Y, Chau K, Wu X-Y (2006) Using genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedure. J Hydrol 316(1–4):129–140
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Deo RC, Şahin M (2015) Application of the extreme learning machine algorithm for the prediction of monthly effective drought index in eastern Australia. Atmos Res 153:512–525
Dettinger MD, Cayan DR, Meyer MK, Jeton AE (2004) Simulated hydrologic responses to climate variations and change in the Merced, Carson, and American River basins, Sierra Nevada, California, 1900–2099. Clim Chang 62(1–3):283–317
Dibike YB, Velickov S, Solomatine D, Abbott MB (2001) Model induction with support vector machines: introduction and applications. J Comput Civ Eng 15(3):208–216
Ding S, Zhao H, Zhang Y, Xu X, Nie R (2015) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44(1):103–115
Genç O, Dağ A (2016) A machine learning-based approach to predict the velocity profiles in small streams. Water Resour Manag 30(1):43–61
Ghumman AR, Ahmad S, Hashmi HN (2018) Performance assessment of artificial neural networks and support vector regression models for stream flow predictions. Environ Monit Assess 190(12):704
Gizaw MS, Gan TY (2016) Regional flood frequency analysis using support vector regression under historical and future climate. J Hydrol 538:387–398
Govindaraju RS (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 5(2):124–137
Goyal MK, Bharti B, Quilty J, Adamowski J, Pandey A (2014) Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, fuzzy logic, and ANFIS. Expert Syst Appl 41(11):5267–5276
Grantz K, Rajagopalan B, Zagona E, Clark M (2007) Water management applications of climate-based hydrologic forecasts: case study of the Truckee-Carson River basin. J Water Resour Plan Manag 133(4):339–350
Guo J, Zhou J, Qin H, Zou Q, Li Q (2011) Monthly streamflow forecasting based on improved support vector machine model. Expert Syst Appl 38(10):13073–13081
Hadi SJ, Tombul M (2018) Forecasting daily streamflow for basins with different physical characteristics through data-driven methods. Water Resour Manag 32(10):3405–3422
Hay LE, Wilby RL, Leavesley GH (2000) A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States 1. JAWRA J Am Water Res Assoc 36(2):387–397
Henning JA, Gresswell RE, Fleming IA (2007) Use of seasonal freshwater wetlands by fishes in a temperate river floodplain. J Fish Biol 71(2):476–492
Hu T, Lam K, Ng S (2001) River flow time series prediction with a range-dependent neural network. Hydrol Sci J 46(5):729–745
Huang G-B (2014) An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput 6(3):376–390
Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cyber Part B (Cybernetics) 42(2):513–529
Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw 2:985–990
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5(2):115–123
Jeton AE, Dettinger MD, Smith J (1996) Potential effects of climate change on streamflow, eastern and western slopes of the Sierra Nevada, California and Nevada. Water Resourc Invest Rep 95:4260
Kalra A, Ahmad S, Nayak A (2013) Increasing streamflow forecast lead time for snowmelt-driven catchment based on large-scale climate patterns. Adv Water Resour 53:150–162
Kang K, Lee JH (2014) Hydrologic modelling of the effect of snowmelt and temperature on a mountainous watershed. J Earth Syst Sci 123(4):705–713
Karran DJ, Morin E, Adamowski J (2013) Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes. J Hydroinf 16(3):671–689
Kimbrough R, Ruppert G, Wiggins W, Smith R, Kresch D (2006) Water resources data-Washington water year 2005. U. S. Geological Survey
Kumar D, Pandey A, Sharma N, Flügel W-A (2016) Daily suspended sediment simulation using machine learning approach. Catena 138:77–90
Lafdani EK, Nia AM, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62
Lin J-Y, Cheng C-T, Chau K-W (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51(4):599–612
Liong SY, Sivapragasam C (2002) Flood stage forecasting with support vector machines 1. JAWRA J Am Water Res Assoc 38(1):173–186
Liu M, Lu J (2014) Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river? Environ Sci Pollut Res 21(18):11036–11053
Liu Y, Sang Y-F, Li X, Hu J, Liang K (2017) Long-term streamflow forecasting based on relevance vector machine model. Water 9(1):9
Luo X, Yuan X, Zhu S, Xu Z, Meng L, Peng J (2019) A hybrid support vector regression framework for streamflow forecast. J Hydrol 568:184–193
Maity R, Bhagwat PP, Bhatnagar A (2010) Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrol Proc: Int J 24(7):917–923
Mauger G, Lee S-Y, Bandaragoda C, Serra Y, Won J (2016) Effect of climate change on the Hydrology of the Chehalis Basin. Prepared for anchor QEA. Climate Impacts Group, University of Washington, Seattle
Meng E, Huang S, Huang Q, Fang W, Wu L, Wang L (2019) A robust method for non-stationary streamflow prediction based on improved EMD-SVM model. J Hydrol 568:462–478
Niu W-j, Feng Z-k, Zeng M, Feng B-f, Min Y-w, Cheng C-t et al (2019) Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm. Appl Soft Comput 82:105589
Noori N, Kalin L (2016) Coupling SWAT and ANN models for enhanced daily streamflow prediction. J Hydrol 533:141–151
Patel SS, Ramachandran P (2015) A comparison of machine learning techniques for modeling river flow time series: the case of upper Cauvery river basin. Water Resour Manag 29(2):589–602
Peng T, Zhou J, Zhang C, Fu W (2017) Streamflow forecasting using empirical wavelet transform and artificial neural networks. Water 9(6):406
Ragettli S, Cortés G, McPhee J, Pellicciotti F (2014) An evaluation of approaches for modelling hydrological processes in high-elevation, glacierized Andean watersheds. Hydrol Process 28(23):5674–5695
Rezaie-Balf M, Zahmatkesh Z, Kim S (2017) Soft computing techniques for rainfall-runoff simulation: local non–parametric paradigm vs. model classification methods. Water Resour Manag 31(12):3843–3865
Şahin M, Kaya Y, Uyar M, Yıldırım S (2014) Application of extreme learning machine for estimating solar radiation from satellite data. Int J Energy Res 38(2):205–212
Sapin J, Rajagopalan B, Saito L, Caldwell RJ (2017) A K-nearest neighbor based stochastic multisite flow and stream temperature generation technique. Environ Model Softw 91:87–94
Shortridge JE, Guikema SD, Zaitchik BF (2016) Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds. Hydrol Earth Syst Sci 20(7):2611–2628
Shrestha N, Shukla S (2015) Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment. Agric For Meteorol 200:172–184
Sudheer K, Gosain A, Ramasastri K (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16(6):1325–1330
Tongal H, Booij MJ (2018) Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. J Hydrol 564:266–282
Wang L, Li X, Ma C, Bai Y (2019) Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy. J Hydrol 573:733–745
Wang W-C, Chau K-W, Cheng C-T, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306
Wang W (2006) Stochasticity, nonlinearity and forecasting of streamflow processes. IOS Press, Amsterdam
Wu C, Chau K, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389(1–2):146–167
Wu JS, Han J, Annambhotla S, Bryant S (2005) Artificial neural networks for forecasting watershed runoff and stream flows. J Hydrol Eng 10(3):216–222
Wu K-P, Wang S-D (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn 42(5):710–717
Yang T, Asanjan AA, Welles E, Gao X, Sorooshian S, Liu X (2017) Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. Water Resour Res 53(4):2786–2812
Yaseen ZM, El-Shafie A, Jaafar O, Afan HA, Sayl KN (2015) Artificial intelligence based models for stream-flow forecasting: 2000–2015. J Hydrol 530:829–844
Yoon H, Hyun Y, Lee K-K (2007) Forecasting solute breakthrough curves through the unsaturated zone using artificial neural networks. J Hydrol 335(1–2):68–77
Yoon H, Jun S-C, Hyun Y, Bae G-O, Lee K-K (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138
Yu P-S, Chen S-T, Chang I-F (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3–4):704–716
Yu P-S, Yang T-C, Chen S-Y, Kuo C-M, Tseng H-W (2017) Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. J Hydrol 552:92–104
Yu X, Liong S-Y (2007) Forecasting of hydrologic time series with ridge regression in feature space. J Hydrol 332(3–4):290–302
Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214(1–4):32–48
Zhang D, Lin J, Peng Q, Wang D, Yang T, Sorooshian S, Liu X, Zhuang J (2018) Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm. J Hydrol 565:720–736
Zhu Q-Y, Qin AK, Suganthan PN, Huang G-B (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763
Acknowledgments
We appreciate the U.S Geological Survey for providing the daily streamflow data of the four stations in this study. We would also like to express our appreciation to the National Oceanic and Atmospheric Administration (NOAA) for providing the daily historical weather information. This work was supported by the National Research Foundation of Korea (NRF) grant (2018R1A2B6001799) funded by the Korean Government (MEST).
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Parisouj, P., Mohebzadeh, H. & Lee, T. Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States. Water Resour Manage 34, 4113–4131 (2020). https://doi.org/10.1007/s11269-020-02659-5
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DOI: https://doi.org/10.1007/s11269-020-02659-5