Abstract
Ideal prediction and modeling of stream-flow and its hydrological applications are extremely significant for decision-making tasks and proper planning of water resource and hydraulic engineering. In the last two decades, the potential of soft computing approaches has increased dramatically in engineering and science problems. In this research, the utility of two soft computing approaches, namely support vector regression (SVR) model and generalized regression neural network (GRNN), is validated to predict 1 day ahead daily river flow data in the upper Senegal River basin at the Bafing Makana station in West Africa. The modeling is conducted by including the climatological information in the modeled stream-flow patterns. Correlation procedure is established and applied to obtain the modeling of the input variables with statistically significant lagged datasets at t − 1, t − 2, and t − 3 used as three input combination for each case study scenario. Different statistical indicators are used to evaluate the accuracy of the prediction models. The results show that the accuracy of the models varied by the scenario and the input datasets, where the SVR model yielded the best results for both modeling scenarios. It is also evident that combining the historical stream-flow data with the rainfall and evapotranspiration can ameliorate substantially the accuracy of the two models for predicting 1-day ahead stream-flow. A comparison of the optimal SVR and GRNN models in this problem indicates that SVR exhibits superior performance to the GRNN model in estimating the daily stream-flow data, irrespective of the modeling scenario and the datasets that is applied. The findings offer an opportunity to apply SVR model for predicting daily stream-flow, with less data requirement for the investigated Senegal River basin.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Babu CN, Reddy BE (2014) A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Appl Soft Comput 23:27–38. https://doi.org/10.1016/j.asoc.2014.05.028
Badrzadeh H, Sarukkalige R, Jayawardena AW (2013) Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting. J Hydrol 507:75–85. https://doi.org/10.1016/j.jhydrol.2013.10.017
Bodian A, Dezetter A, Deme A, Diop L (2016) Hydrological evaluation of TRMM rainfall over the upper Senegal River basin. Hydrology 3:15. https://doi.org/10.3390/hydrology3020015
Ch S, Anand N, Panigrahi BK, Mathur S (2013) Stream-flow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23. https://doi.org/10.1016/j.neucom.2012.07.017
Cheng C, Niu W, Feng Z et al (2015) Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization. Water 7:4232–4246. https://doi.org/10.3390/w7084232
Deo RC, Wen X, Qi F (2016) A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Appl Energy 168:568–593. https://doi.org/10.1016/j.apenergy.2016.01.130
Dione O (1996) Evolution Climatique Récente et Dynamique Fluviale dans les Hauts Bassins des Fleuves Sénégal et Gambie. Science et changements planétaires/Sécheresse 8:300–301
Fahimi F, Yaseen ZM, El-shafie A (2016) Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theor Appl Climatol. https://doi.org/10.1007/s00704-016-1735-8
Ghorbani MA, Zadeh HA, Isazadeh M, Terzi O (2016) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environ Earth Sci 75:1–14. https://doi.org/10.1007/s12665-015-5096-x
Gong Y, Zhang Y, Lan S, Wang H (2016) A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida. Water Resour Manag 30:375–391
Guimarães Santos CA, Da Silva GBL (2014) Daily stream-flow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrol Sci J 59:312–324
Hannan SA, Manza RR, Ramteke RJ (2010) Generalized regression neural network and radial basis function for heart disease diagnosis. Int J Comput Appl 7:975–8887. https://doi.org/10.5120/1325-1799
Jain SK (2012) Modeling river stage–discharge–sediment rating relation using support vector regression. Hydrol Res 43:851–861. https://doi.org/10.2166/nh.2011.101
Kagoda PA, Ndiritu J J, Ntuli C, Mwaka C (2010) Application of radial basis function neural networks to short-term stream-flow forecasting. Phys Chem Earth 35:571–581. https://doi.org/10.1016/j.pce.2010.07.021
Kane H, Diallo A (2005) Etude portant sur l’évaluation de l’état de l’environnement des ressources naturelles et des ressources en eau dans la partie guinéenne du bassin du fleuve Sénégal, en se servant du système d’indicateurs de l’Observatoire de l’environnement de l’OMVS. OMVS Report, Dakar, 2005 (In French)
Kashid SS, Ghosh S, Maity R (2010) Stream-flow prediction using multi-site rainfall obtained from hydroclimatic teleconnection. J Hydrol 395:23–38. https://doi.org/10.1016/j.jhydrol.2010.10.004
Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351:299–317. https://doi.org/10.1016/j.jhydrol.2007.12.014
Kim S, Shiri J, Kisi O (2012) Pan evaporation modeling using neural computing approach for different climatic zones. Water Resour Manag 26:3231–3249. https://doi.org/10.1007/s11269-012-0069-2
Kisi Ö (2006) Generalized regression neural networks for evapotranspiration modelling. Hydrol Sci J 51:1092–1105
Kisi O, Cimen M (2011) A wavelet-support vector machine conjunction model for monthly stream-flow forecasting. J Hydrol 399:132–140. https://doi.org/10.1016/j.jhydrol.2010.12.041
Kumar PS, Praveen TV, Prasad MA (2016) Artificial neural network model for rainfall-runoff: a case study. Int J Hybrid Inf Technol 9:263–272
Kuo CC, Gan TY, Yu PS (2010) Seasonal stream-flow prediction by a combined climate-hydrologic system for river basins of Taiwan. J Hydrol 387:292–303. https://doi.org/10.1016/j.jhydrol.2010.04.020
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. https://doi.org/10.1007/s11356-014-3046-x
Makwana JJ, Tiwari MK (2014) Intermittent stream-flow forecasting and extreme event modelling using wavelet based artificial neural networks. Water Resour Manag 28:4857–4873. https://doi.org/10.1007/s11269-014-0781-1
Ndiaye O (2010) The predictability of the sahelian climate: seasonal sahel rainfall and onset over Senegal. Columbia University, Columbia
Ni Q, Wang L, Ye R et al (2010) Evolutionary modeling for stream-flow forecasting with minimal datasets: a case study in the west Malian river, China. Environ Eng Sci 27:377–385. https://doi.org/10.1089/ees.2009.0082
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
Prairie JR, Rajagopalan B, Fulp TJ, Zagona EA (2006) Modified K-NN model for stochastic stream-flow simulation. J Hydrol Eng 11:371–378. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:4(371)
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
Rubio G, Pomares H, Rojas I, Herrera LJ (2011) A heuristic method for parameter selection in LS-SVM: application to time series prediction. Int J Forecast 27:725–739. https://doi.org/10.1016/j.ijforecast.2010.02.007
Shiri J, Kisi O (2010) Short-term and long-term stream-flow forecasting using a wavelet and neuro-fuzzy conjunction model. J Hydrol 394:486–493. https://doi.org/10.1016/j.jhydrol.2010.10.008
Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576. https://doi.org/10.1109/72.97934
Sujay RN, Deka PC (2015) Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression. Cogent Eng 2(1). https://doi.org/10.1080/23311916.2014.999414
Taormina R, Chau KW (2015) ANN-based interval forecasting of stream-flow discharges using the LUBE method and MOFIPS. Eng Appl Artif Intell 45:429–440. https://doi.org/10.1016/j.engappai.2015.07.019
Tayyab M, Zhou J, Zeng X, Adnan R (2016) Discharge forecasting by applying artificial neural networks at the Jinsha river basin, China. Eur Sci J 12:1857–7881. https://doi.org/10.19044/esj.2016.v12n9p108
Toth E, Brath A (2007) Multistep ahead stream-flow forecasting: role of calibration data in conceptual and neural network modeling. Water Resour Res 43:1–11. https://doi.org/10.1029/2006WR005383
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
Varis O, Lahtela V (2002) Integrated water resources management along the Senegal River: introducing an analytical framework. Int J Water Resour Dev 18:501–521. https://doi.org/10.1080/0790062022000017374
Wang W, Van Gelder PH, Vrijling JK, Ma J (2006) Forecasting daily stream-flow using hybrid ANN models. J Hydrol 324:383–399. https://doi.org/10.1016/j.jhydrol.2005.09.032
Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306. https://doi.org/10.1016/j.jhydrol.2009.06.019
Wen X, Si J, He Z et al (2015) Support-vector-machine-based models for modeling daily reference evapotranspiration with limited climatic data in extreme arid regions. Water Resour Manag 29:3195–3209. https://doi.org/10.1007/s11269-015-0990-2
Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194
Yaseen ZM, El-Shafie A, Afan HA et al (2015a) RBFNN versus FFNN for daily river flow forecasting at Johor river, Malaysia. Neural Comput Appl. https://doi.org/10.1007/s00521-015-1952-6
Yaseen ZM, El-shafie A, Jaafar O et al (2015b) 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, Jaafar O, Deo RC et al (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol. https://doi.org/10.1016/j.jhydrol.2016.09.035
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Rights and permissions
About this article
Cite this article
Diop, L., Bodian, A., Djaman, K. et al. The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River. Environ Earth Sci 77, 182 (2018). https://doi.org/10.1007/s12665-018-7376-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-018-7376-8