Abstract
The forecast of river flow has high great importance in water resources and hazard management. It becomes more important in mountain areas because most of the downstream populations have high dependency for their livelihood, agriculture, and commercial activities like hydro power production. In this context, in recent times, machine learning models have got high attention due to their high accuracy in forecasting through self-learning from physical processes. In this work, we consider the potential of a data driven methods of machine learning, namely multilayer perceptron (MLP), support vector regression (SVR), and random forest (RF), are explored to forecast Hunza river flow in Pakistan using in-situ dataset for the period from 1962 to 2008. A set of five input combinations with lagged river flow values are developed based on the autocorrelation (ACF) and partial autocorrelation function (PACF) on historical river flow data. A comparative investigation is conducted to assess the performance of MLP, SVR and RF. The results of machine learning models are compared using forecasting metrics defined as correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and mean square error (MSE) between the observed and forecasted river flow data to assess the models’ effectiveness. The results show that RF performed the best followed by MLP and SVR. In measurable terms, superiority of RF over MPL and SVR models was demonstrated by R2 = 0.993, 0.910, and 0.831, RMSE = 0.069, 0.084, and 0.104, MAE = 0.040, 0.058, and 0.062, respectively. The RF model performed 33.6% better than SVR and 17.85% to MLP. The results strengthen the argument that machine learning algorithms/models particularly RF model can be used for forecasting rivers flow with high accuracy which will further improve water and hazard management.
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References
Alvisi S, Franchini M (2011) Fuzzy neural networks for water level and discharge forecasting with uncertainty. Environ Model Softw 26(4):523–537. https://doi.org/10.1016/j.envsoft.2010.10.016
Asefa T, Kemblowski M, McKee M, Khalil A (2006) Multi-time scale stream flow predictions: the support vector machines approach. J Hydrol 318(1–4):7–16. https://doi.org/10.1016/j.jhydrol.2005.06.001
Baig SU, Tahir AA, Din A, Khan H (2018) Hypsometric properties of mountain landscape of Hunza River basin of the Karakoram Himalaya. J Mt Sci 15(9):1881–1891. https://doi.org/10.1007/s11629-018-4849-x
Bajracharya SR, Maharjan SB, Shrestha F, Guo W, Liu S, Immerzeel W, Shrestha B (2015) The glaciers of the Hindu Kush Himalayas: current status and observed changes from the 1980s to 2010. Int J Water Resour Dev 31(2):161–173. https://doi.org/10.1080/07900627.2015.1005731
Bharti B, Pandey A, Tripathi SK, Kumar D (2017) Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models. Hydrol Res 48(6):1489–1507. https://doi.org/10.2166/nh.2017.153
Biau G, Scornet E (2016) A random forest guided tour. Test 25(2):197–227. https://doi.org/10.1007/s11749-016-0481-7
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
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. https://doi.org/10.1623/hysj.48.3.381.45286
Chen ST, Yu PS, Tang YH (2010) Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. J Hydrol 385(1–4). https://doi.org/10.1016/j.jhydrol.2010.01.021
Cigizoglu HK, Alp M (2004) Rainfall-runoff modelling using three neural network methods. In: Rutkowski L, Siekmann JH, Tadeusiewicz R, Zadeh LA (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. Lecture Notes in Computer Science, vol 3070. Springer, Berlin, Heidelberg
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1007/bf00994018
Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314. https://doi.org/10.1007/BF02551274
Darbandi S, Pourhosseini FA (2018) River flow simulation using a multilayer perceptron-firefly algorithm model. Appl Water Sci 8(3):1–9. https://doi.org/10.1007/s13201-018-0713-y
Granata F, Papirio S, Esposito G, Gargano R, de Marinis G (2017) Machine learning algorithms for the forecasting of wastewater quality indicators. Water (Switzerland) 9(2). https://doi.org/10.3390/w9020105
Granata F, Saroli M, De Marinis G, Gargano R (2018) Machine learning models for spring discharge forecasting. Geofluids 2018. https://doi.org/10.1155/2018/8328167
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. https://doi.org/10.1016/j.eswa.2011.04.114
Haykin S (2001) Neural networks and learning machines third edition. Angew Chem Int Ed 40(6). https://doi.org/10.1002/1521-3773(20010316)40:6<9823::AID-ANIE9823>3.3.CO;2-C
He Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386. https://doi.org/10.1016/j.jhydrol.2013.11.054
Hewitt K, Wake CP, Young GJ, David C (1989) Hydrological investigations at Biafo glacier, Karakoram range, Himalaya: an important source of water for the Indus River. Ann Glaciol 13:103–108. https://doi.org/10.3189/s0260305500007710
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5). https://doi.org/10.1016/0893-6080(89)90020-8
Imani M, Kao HC, Lan WH, Kuo CY (2018) Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine. Glob Planet Chang 161:211–221. https://doi.org/10.1016/j.gloplacha.2017.12.018
Kashani MH, Ghorbani MA, Dinpazhouh Y, Shahmorad S (2016) Rainfall-Runoff simulation in the Navrood river basin using truncated volterra model and artificial neural networks. Journal of Watershed Management Research 6(12):1–10
Khan MS, Coulibaly P (2006) Application of support vector machine in lake water level prediction. J Hydrol Eng 11(3):199–205. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:3(199)
Khan AA, Jamil A, Hussain D, Taj M, Jabeen G, Malik MK (2020) Machine-learning algorithms for mapping debris-covered glaciers: the Hunza Basin case study. IEEE Access 8:12725–12734. https://doi.org/10.1109/ACCESS.2020.2965768
Krajewski WF, Ceynar D, Demir I, Goska R, Kruger A, Langel C, Mantilllla R, Niemeier J, Quintero F, Seo BC, Smallll SJ, Weber LJ, Young NC (2017) Real-time flood forecasting and information system for the state of Iowa. Bull Am Meteorol Soc 98(3):539–554. https://doi.org/10.1175/BAMS-D-15-00243.1
Lee EH, Kim JH, Choo YM, Jo DJ (2018) Application of flood nomograph for flood forecasting in urban areas. Water (Switzerland) 10(1). https://doi.org/10.3390/w10010053
Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241. https://doi.org/10.1029/1998WR900018
Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2(3):8–22
Lin J, Cheng C, Chau K (2006) Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal 51(4):599–612
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133. https://doi.org/10.1007/BF02478259
More D, Magar RB, Jothiprakash V (2019) Intermittent reservoir daily inflow prediction using stochastic and model tree techniques. JInst. Eng. India Ser. A 100:439. https://doi.org/10.1007/s40030-019-00368-w
Muhammad R, Yuan X, Kisi O, Yuan Y (2017) Streamflow forecasting using artificial neural network and support vector machine models. American Scientific Research Journal for Engineering, Technology, and Sciences 29(1):286–294
Muñoz P, Orellana-Alvear J, Willems P, Célleri R (2018) Flash-flood forecasting in an andean mountain catchment-development of a step-wise methodology based on the random forest algorithm. Water (Switzerland) 10(11). https://doi.org/10.3390/w10111519
Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet-artificial intelligence models in hydrology: a review. J Hydrol 514:358–377. https://doi.org/10.1016/j.jhydrol.2014.03.057
Nourani V, Davanlou Tajbakhsh A, Molajou A, Gokcekus H (2019) Hybrid wavelet-M5 model tree for rainfall-runoff modeling. J Hydrol Eng 24(5). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001777
Pianosi F, Thi XQ, Soncini-Sessa R (2011) Artificial neural networks and multi objective genetic algorithms for water resources management: an application to the Hoabinh reservoir in Vietnam. IFAC Proceedings Volumes (IFAC-PapersOnline), 44(1 PART 1), 10579–10584. https://doi.org/10.3182/20110828-6-IT-1002.02208
Raghavendra S, Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput J 19:372–386. https://doi.org/10.1016/j.asoc.2014.02.002
Sulaiman M, El-Shafie A, Karim O, Basri H, Sulaiman M, El-Shafie A, Karim O, Basri H (2011) Improved water level forecasting performance by using optimal steepness coefficients in an artificial neural network. Water Resour Manag 25:2525–2541. https://doi.org/10.1007/s11269-011-9824-z
Tiwari MK, Adamowski J (2013) Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resour Res 49(10):6486–6507. https://doi.org/10.1002/wrcr.20517
Tiwari MK, Chatterjee C (2011) A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting. J Hydroinf 13(3):500–519. https://doi.org/10.2166/hydro.2010.142
Tongal H, Booij MJ (2018) Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. J Hydrol 564:266–282. https://doi.org/10.1016/j.jhydrol.2018.07.004
Unal B, Mamak M, Seckin G, Cobaner M (2010) Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels. Adv Eng Softw 41(2):120–129. https://doi.org/10.1016/j.advengsoft.2009.10.002
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. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:3(216)
Wu CL, Chau KW, Li YS (2008) River stage prediction based on a distributed support vector regression 3. J Hydrol 358(2)
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. https://doi.org/10.1016/j.jhydrol.2015.10.038
Yaseen ZM, Allawi MF, Yousif AA, Jaafar O, Hamzah FM, El-Shafie A (2018) Non-tuned machine learning approach for hydrological time series forecasting. Neural Comput & Applic 30(5):1479–1491. https://doi.org/10.1007/s00521-016-2763-0
Young GJ, Hewitt K (1990) Hydrology research in the upper Indus basin, Karakoram Himalaya, Pakistan. IAHS Publications 190:139–152
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Hussain, D., Khan, A.A. Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan. Earth Sci Inform 13, 939–949 (2020). https://doi.org/10.1007/s12145-020-00450-z
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DOI: https://doi.org/10.1007/s12145-020-00450-z