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Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test

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Abstract

In this study, daily rainfall-runoff modeling was done using co-active neuro-fuzzy inference system (CANFIS) and multi-layer perceptron neural network (MLPNN) approaches in the hilly Naula watershed of Ramganga River in Uttarakhand, India. The daily observed rainfall and runoff data from June 1, 2000, to October 31, 2004, were used for training and testing of the applied models. Before starting the modeling process, the gamma test (GT) was used to select the best combination of input variables for each model. The simulated values of runoff from CANFIS and MLPNN models were compared with the observed ones with respect to root mean squared error (RMSE), Nash-Sutcliffe efficiency (CE), Pearson correlation coefficient (PCC). This study provides a conclusive evidence that the CANFIS shows better accuracy than the MLPNN models. Therefore, according to the best fitting CANFIS-10 model, the runoff of the present day depends on rainfall and runoff of current and previous 2 days for the studied area.

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References

  • Aytek A (2009) Co-active neuro fuzzy inference system for evapotranspiration modeling. Soft Comput 13(7):691–700

    Article  Google Scholar 

  • Aytek A, Alp M (2008) Application of artificial intelligence for rainfall–runoff modelling. J Earth Syst Sci 117(2):145–155

    Article  Google Scholar 

  • Chen J, Adams BJ (2006) Integration of artificial neural networks with conceptualmodels in rainfall-runoff modeling. J Hydrol 318:232–249

    Article  Google Scholar 

  • Dawson CW, Wilby RL (1998) Artificial neural network approach to rainfall-runoff modelling. Hydrol Sci J 43(1):47–66

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Haykin S (1998) Neural networks—a comprehensive foundation, second edn. Prentice-Hall, Upper Saddle River, pp 26–32

  • Jain A, Sudheer KP, Srinivasulu S (2004) Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrol Process 18:571–581

    Article  Google Scholar 

  • Jang JR, Sun CT, Mizutani E (1997) Neuro fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, NJ, USA, p 607

    Google Scholar 

  • Kisi O, Shiri J, Tombul M (2013) Modelling rainfall-runoff process using soft computing technique. Comput Geosci 51:108–177

    Article  Google Scholar 

  • Machado F, Mine M, Kaviski E, Fill H (2011) Monthly rainfall–runoff modelling using artificial neural networks. Hydrol Sci J 56(3):349–361

    Article  Google Scholar 

  • Malik A, Kumar A (2015) Pan evaporation simulation using daily meteorological by soft computing techniques and multiple linear regression. Water Resour Manag 29(6):1859–1872

    Article  Google Scholar 

  • Malik A, Kumar A, Piri J (2017) Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India. Comput Electron Agric 138:20–28

    Article  Google Scholar 

  • Mehr AD, Nourani N (2017) A Pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling. Environ Model Softw 92:239–251

    Article  Google Scholar 

  • Modarres R (2008) Multi-criteria validation of artificial neural network rainfall-runoff modelling. Hydrol Earth Syst Sci 13:411–442

    Article  Google Scholar 

  • Moghaddamnia A, Ghafari GM, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88–97

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: Part 1. A discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Nasri M, Modarres R, Dastorani MT (2008) Rainfall-runoff relationship of Zayandehrud dam basin by multi-layer perceptron neural network. J Environ Res Develop 2(4):790–795

    Google Scholar 

  • Nayak PC, Sudheer KP, Jain SK (2007) Rainfall-runoff modeling through hybrid intelligent system. Water Resour Res 43(7):1–17

    Article  Google Scholar 

  • Nayak PC, Venkatesh B, Krishna B, Jain SK (2013) Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach. J Hydrol 493:57–67

    Article  Google Scholar 

  • Nourani V, Kisi O, Komasi M (2011) Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process. J Hydrol 402:41–59

    Article  Google Scholar 

  • Phukoetphim P, Shamseldin AY, Adams K (2016) Multimodel approach using neural networks and symbolic regression to combine the estimated discharges of rainfall-runoff models. J Hydrol Eng 21(8):1–18

    Article  Google Scholar 

  • Piri J, Amin S, Moghaddamnia A, Keshavarz A, Han D, Remesan R (2009) Daily pan evaporation modeling in a hot and dry climate. J Hydrol Eng 14:803–812

    Article  Google Scholar 

  • Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfall-runoff relationship with artificial neural network. J Hydrol 285:96–113

    Article  Google Scholar 

  • Rashidi S, Vafakhah M, Kakaei-Lafdani E, Javadi MR (2016) Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arab J Geosci 9:2–15

    Article  Google Scholar 

  • Remesan R, Shamim MA, Han D, Mathew J (2009) Runoff prediction using an integrated hybrid modelling scheme. J Hydrol 372:48–60

    Article  Google Scholar 

  • Riad S, Mania J, Bouchaou L, Najjar Y (2004) Rainfall-runoff model using an artificial neural network approach. Math Comput Model 40:839–846

    Article  Google Scholar 

  • Shoaib M, Shamseldin AY, Khan S, Khan MM, Khan ZM, Sultan T, Melville BW (2018) A comparative study of various hybrid wavelet feedforward neural network models for runoff forecasting. Water Res Manage 32(1):83–103

    Article  Google Scholar 

  • Solomatine DP, Dulal KN (2003) Model trees as an alternative to neural networks in rainfall–runoff modelling. Hydrol Sci 48(3):399–411

    Article  Google Scholar 

  • Song X-M, Kong F-Z, Zhan C-Z, Han J-W (2012) Hybrid optimization rainfall-runoff simulation based on Xinanjiang model and artificial neural network. J Hydrol Eng 17:1033–1041

    Article  Google Scholar 

  • Stefansson A, Koncar N, Jones AJ (1997) A note on the gamma test. Neural Comput Applic 5:131–133

    Article  Google Scholar 

  • Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16:1325–1330

    Article  Google Scholar 

  • Tabari H, Talaee PH, Abghari H (2012) Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron. Meteorog Atmos Phys 116:147–154

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Manag Cybern 15(1):116–132

    Article  Google Scholar 

  • Tayfur G, Zucco G, Brocca L, Moramarco T (2014) Coupling soil moisture and precipitation observations for predicting hourly runoff at small catchment scale. J Hydrol 510:363–371

    Article  Google Scholar 

  • Trajkovic S, Stankovic M, Todorovic B (2000) Estimation of FAO Blaney-Criddle b factor by RBF network. J Irri Drain Eng 126(4):268–271

    Article  Google Scholar 

  • Tsui AMP, Jones AJ, De-Oliveira AG (2002) The construction of smooth models using irregular embeddings determined by a gamma test analysis. Neural Comput Appl 10(4):318–329

    Article  Google Scholar 

  • Zounemat-Kermani M, Kisi O, Rajaee T (2013) Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Appl Soft Computing 13(12):4633–4644

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India

    Anita Singh, Anurag Malik & Anil Kumar

  2. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

Authors
  1. Anita Singh
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  2. Anurag Malik
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  3. Anil Kumar
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  4. Ozgur Kisi
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Correspondence to Ozgur Kisi.

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Singh, A., Malik, A., Kumar, A. et al. Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test. Arab J Geosci 11, 261 (2018). https://doi.org/10.1007/s12517-018-3614-3

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  • DOI: https://doi.org/10.1007/s12517-018-3614-3

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