Hydrological flow rate estimation using artificial neural networks: Model development and potential applications

Applied Mathematics and Computation

Volumn 291, Issue , 2016, Pages 373-385

  Kostić, Srđan ^a   Stojković, Milan ^a   Prohaska, Stevan ^a  

^a Institute for Development of Water Resources Jaroslav erni''   (Serbia)

Author keywords

Artificial neural networks; Climatic scenario; Flow rate; Hydroenergy production; Monte Carlo simulation

Indexed keywords

ELECTRIC POWER GENERATION; ELECTRIC POWER SYSTEMS; ELECTRIC UTILITIES; FLOW RATE; FORECASTING; INTELLIGENT SYSTEMS; MONTE CARLO METHODS; NEURAL NETWORKS; WATER MANAGEMENT;

CLIMATIC SCENARIOS; DETERMINATION COEFFICIENTS; ELECTRIC POWER PRODUCTION; EXPERIMENTAL ERRORS; FLOW RATE ESTIMATIONS; MULTI-LAYER FEED FORWARD; NUMBER OF HIDDEN NEURONS; REGIONAL CLIMATE MODELING;

CLIMATE MODELS;

EID: 84979656196     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2016.07.014     Document Type: Article

References (50)

1. [1] Fister, I., Suganthan, P.N., Fister, I. Jr., Kamal, S.M., Al-Marzouki, F.M., Perc, M., Strnad, D., Artificial neural network regression as a local search heuristic for ensemble strategies in differential evolution. Nonlinear Dyn. 84 (2016), 895–914.
2. [2] Fister, I. Jr., Ljubič, K., Suganthan, P.N., Perc, M., Fister, I., Computational intelligence in sports: challenges and opportunities within a new research domain. Appl. Math. Comput. 262 (2015), 178–186.
3. [3] Gokceoglu, C., Sezer, E., Soft computing modeling in landslide susceptibility assessment. Pradhan, B., Buchroithner, M., (eds.) Terrigenous Mass Movements: Detection, Modelling, Early Warning and Mitigation Using Geoinformation Technology, 2012, Springer, 51–90.
4. [4] Gelisli, K., Kaya, T., Babacan, A.E., Assessing the factor of safety using an artificial neural network: case studies on landslides in Giresun, Turkey. Environ. Earth Sci. 73 (2015), 8639–8646.
5. [5] Morales-Esteban, A., Martinez-Alvarez, F., Reyes, J., Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence. Tectonophysics 593 (2013), 121–134.
6. [6] Alimoradi, A., Beck, J., Machine-learning methods for earthquake ground motion analysis and simulation. J. Eng. Mech., 141, 2015, 04014147.
7. [7] Madhubabu, L.N., Singh, P.K., Kainthola, A., Mahanta, B., Tripathy, A., Singh, T.N., Prediction of compressive strength and elastic modulus of carbonate rocks. Measurement 88 (2016), 202–213.
8. [8] Tripathy, A., Singh, T.N., Kundu, J., Prediction of Abrasiveness index of some Indian Rocks using soft computing methods. Measurement 68 (2015), 302–309.
9. [9] Kainthola, A., Singh, P.K., Verma, D., Singh, R., Sarkar, K., Singh, T.N., Prediction of strength parameters of Himalayan rocks: a statistical and ANFIS approach. Geotech. Geol. Eng. 33 (2015), 1255–1278.
10. [10] Kumar, D.N., Srinivasa, K.R., Sathish, T., River flow forecasting using recurrent neural networks. Water Resour. Manag. 18 (2004), 143–161.
11. [11] Huang, W., Xu, B., Chan-Hilton, A., Forecasting flows in Apalachicola River using neural networks. Hydrol. Process. 18 (2004), 2545–2564.
12. [12] Aqil, M., Kita, I., Yano, A., Nishiyama, S., Neural networks for real time catchment flow modeling and prediction. Water Resour. Manag. 21 (2007), 1781–1796.
13. [13] Zounemat-Kermani, M., Kisi, O., Rajaee, T., Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Appl. Soft Comput. 13 (2013), 4633–4644.
14. [14] Chen, X.Y., Chau, K.W., Busari, A.O., A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Eng. Appl. Artif. Intell. 46 (2015), 258–268.
15. [15] Maity, R., Bhagwat, P.P., Bhatnagar, A., Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrol. Process. 24 (2010), 917–923.
16. [16] Mehr, D., Kahya, E., Yerdelen, C., Linear genetic programming application for successive-station monthly streamflow prediction. Comput. Geosci. 70 (2014), 63–72.
17. [17] Sivakumar, B., Berndtsson, R., Persson, M., Monthly runoff prediction using phase-space reconstruction. Hydrol. Sci. J. 46 (2001), 377–388.
18. [18] Sivakumar, B., Jayawardena, A.W., Fernando, T.M.K.G., River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. J. Hydrol. 265 (2002), 225–245.
19. [19] Govindaraju, R.S., Rao, A.R., Artificial Neural Networks in Hydrology. 2013, Springer, 1–15.
20. [20] Beven, K., Lamb, R., Quinn, P., Romanowicz, R., Freer, J., Topmodel. Singh, V.P., (eds.) Computer Models of Watershed Hydrology, 1995, Water Resources Publications, Highlands Ranch, CO, 627–668.
21. [21] Bandaragoda, C., Tarboton, D.G., Woods, R., Application of TOPNET in the distributed model Intercomparison project. J. Hydrol. 298 (2004), 178–201.
22. [22] Shen, C., Phanikumar, M.S., A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling. Adv. Water Res. 33 (2010), 1524–1541.
23. [23] Sugawara, M., Watanabe, E., Ozaki, E., Katsuyama, Y., Tank Model with Snow Component. 1984, The National Research Center for Disaster Prevention, Science and Technology Agency, Japan.
24. [24] Todini, E., The ARNO rainfall-runoff model. J. Hydrol. 175 (1996), 339–382.
25. [25] Zealand, C.M., Burn, D.H., Simonović, S.P., Short term streamflow forecasting using artificial neural networks. J. Hydrol. 214 (1999), 32–48.
26. [26] Akhtar, M., Ahmad, N., Booij, J., Use of regional climate model simulations as input for hydrological models for the Hindukush-Karakorum-Himalaya region. Hydrol. Earth Syst. Sci. 13 (2009), 1075–1089.
27. [27] Brocca, L., Barbetta, S., Melone, F., Moramarco, T., A Continuous Rainfall-Runoff Model Derived from Investigations on a Small Experimental Basin, 336, 2010, IAHS Publications, 179–185.
28. [28] Lauri, H., Rasanen, T.A., Kummu, M., Using reanalysis and remotely sensed temperature and precipitation data for hydrological modeling in monsoon climate: Mekong River case study. J. Hydrometeorol. 15 (2014), 1532–1545.
29. [29] Tiryaki, B., Application of artificial neural networks for predicting the cuttability of rocks by drag tools. Tunn. Undergr. Space Technol. 23 (2008), 273–280.
30. [30] Lipmann, R.P., An introduction to computing with neural nets. IEEE ASSP Mag. 4 (1987), 4–22.
31. [31] Moradkhani, H, Hoshin, K.L.H., Gupta, V., Sorooshian, S., Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J. Hydrol. 295 (2004), 246–262.
32. [32] Raghuwanshi, N., Singh, R., Reddy, L., Runoff and sediment yield modeling using artificial neural networks: upper Siwane River, India. J. Hydrol. Eng. 11 (2006), 71–79.
33. [33] Besaw, E.L., Rizzo, D.M., Bierman, P.R., Hackett, W.R., Advances in ungauged streamflow prediction using artificial neural networks. J. Hydrol. 386 (2010), 27–37.
34. [34] Zeng, X., Kundzewicz, Z.W., Zhou, J., Su, B., Discharge projection in the Yangtze River basin under different emission scenarios based on the artificial neural networks. Quat. Int. 282 (2012), 113–121.
35. [35] Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning internal representation by error propagation. Rumelhart, D.E., McCleland, J.L., (eds.) Parallel Distribution Processing, 1, 1986, MIT Press Cambridge, MA, USA, 318–362.
36. [36] Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A., Kayabasi, A., Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int. J. Rock Mech. Min. Sci. 43 (2006), 224–235.
37. [37] Looney, C.G., Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans. Knowl. Data Eng. 8 (1996), 211–226.
38. [38] Nelson, M., Illingworth, W.T., A Practical Guide to Neural Nets. 1990, Addison-Wesley, Reading MA.
39. [39] Prohaska, S., Simić, Z., Orlić, A., Basic hydrographics and hydrological characteristics of the Drina River basin and hydrometeorological data. Vodoprivreda 36 (2004), 21–38 (in Serbian).
40. [40] Republic Hydrometerological Service of Serbia. Hydro-meteorological yearbooks 1950-2012 [ http://www.hidmet.gov.rs/].
41. [41] Đurđević, V., Rajković, B., Verification of a coupled atmosphere-ocean model using satellite observations over the Adriatic Sea. Ann. Geophys. 26 (2008), 1935–1954.
42. [42] Pekarova, P., Pekar, J., Long-term discharge prediction for the Turnu Severin station (the Danube) using a linear autoregressive model. Hydrol. Process. 20 (2006), 1217–1228.
43. [43] Labat, D., Oscillations in land surface hydrological cycle. Earth Planet. Sci. Lett. 242 (2006), 143–154.
44. [44] Stojković, M., Plavšić, J., Despotović, J., Prohaska, S., Pavlović, D., Long-term hydrological projection of monthly flows in terms of climate change adaptation strategy. Proceedings of the 7th Eastern European Young Water Professionals Conference, IWA, Belgrade. Serbia, 2015.
45. [45] Arnell, N., Effects of IPCC SRES emissions scenarios on the river runoff: a global perspective. Hydrol. Earth Syst. Sci. 7 (2003), 619–641.
46. [46] ICPDR, Danube Study – Climate Change Adaptation., 2012, International Commission for the Protection of the Danube River Final Report.
47. [47] Haddeland, I., Effects of Climate Change in the Kolubara and Toplica Catchments, Serbia. Norwegian Water Resources and Energy Directorate. Norway., 2013.
48. [48] IPCC, Fifth Assessment Report (AR5)., 2013, Intergovernmental Panel on Climate Change, Geneva 2. Switzerland http://www.ipcc.ch/report/ar5/index.shtml.
49. [49] World Bank, Water climate and adaptation plan for the Sava River basin. Plavšić, J., (eds.) Development of the Hydrologic Model for the Sava River Basin, 2014, World Bank Group, Washington, D.C. Draft Final Report. Annex 1.
50. [50] Report of hydroenergetic production, Dataset of Hydro-energy Production at HPP ``Potpeć''. 2009, Bajina Bašta, Serbia.