Abstract
Artificial neural network (ANN) is a popular data-driven modelling technique that has found application in river flow forecasting over the last two decades. This can be attributed to its ability to assimilate complex and nonlinear input-output relationships inherent in hydrological processes within a river catchment. However despite its prominence, ANNs are still prone to certain problems such as overfitting and over-parameterization, especially when used under limited availability of datasets. These problems often influence the predictive ability of ANN-derived models, with inaccurate and unreliable results as resultant effects. This paper presents a study aimed at finding a solution to the aforementioned problems. Two evolutionary computational techniques namely differential evolution (DE) and genetic programming (GP) were applied to forecast monthly flow in the upper Mkomazi River, South Africa using a 19-year baseline record. Two case studies were considered. Case study 1 involved the use of correlation analysis in selecting input variables during model development while using DE algorithm for optimization purposes. However in the second case study, GP was incorporated as a screening tool for determining the dimensionality of the ANN models, while the learning process was subjected to sensitivity analysis using the DE-algorithm. Results from the two case studies were evaluated comparatively using three standard model evaluation criteria. It was found that results from case study 1 were considerably plagued by the problems of overfitting and over-parameterization, as significant differences were observed in the error estimates and R2 values between the training and validation phases. However, results from case study 2 showed great improvement, as the overfitting and memorization problems were significantly minimized, thus leading to improved forecast accuracy of the ANN models. It was concluded that the conjunctive use of GP and DE can be used to improve the performance of ANNs, especially when availability of datasets is limited.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdul-Kader, H.: Neural networks training based on differential evolution algorithm compared with other architectures for weather forecasting34. IJCSNS 9(3), 92–99 (2009)
Adeyemo, J., Otieno, F.: Differential evolution algorithm for solving multi-objective crop planning model. Agric. Water Manage. 97(6), 848–856 (2010)
Babovic, V., Keijzer, M.: Rainfall runoff modelling based on genetic programming. Nordic Hydrol. 33(5), 331–346 (2002)
Coulibaly, P., Evora, N.: Comparison of neural network methods for infilling missing daily weather records. J. Hydrol. 341(1), 27–41 (2007)
Elshorbagy, A., Corzo, G., Srinivasulu, S., Solomatine, D.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology part 1: concepts and methodology. Hydrol. Earth Syst. Sci. 14(10), 1931–1941 (2010)
Flugel, W., Marker, M.: The response units concept and its application for the as-sessment of hydrologically related erosion processes in semiarid catchments of Southern Africa. ASTM Spec. Tech. Publ. 1420, 163–177 (2003)
Jha, G.K.: Artificial neural networks and its applications (2007). http://www.iasri.res.in/ebook/ebadat/5-Modeling
Karthikeyan, L., Kumar, D.N., Graillot, D., Gaur, S.: Prediction of ground water levels in the uplands of a tropical coastal riparian wetland using artificial neural networks. Water Resour. Manage. 27(3), 871–883 (2013)
Krse, B., van der Smagt, P.: An Introduction to Neural Networks, 8th edn. The University of Amsterdam, Amsterdam (1996)
Maier, H.R., Dandy, G.C.: Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model. Softw. 15(1), 101–124 (2000)
Ni, Q., Wang, L., Ye, R., Yang, F., Sivakumar, M.: Evolutionary modeling for streamflow forecasting with minimal datasets: a case study in the West Malian River. Environ. Eng. Sci. 27(5), 377–385 (2010)
Nourani, V., Kisi, Ö., Komasi, M.: Two hybrid artificial intelligence approaches for modeling rainfall runoff process. J. Hydrol. 402(1), 41–59 (2011)
Olofintoye, O., Adeyemo, J., Otieno, F.: A combined pareto differential evolution approach for multi-objective optimization. In: EVOLVE-A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation III, pp. 213–231. Springer (2014)
Oyebode, O., Adeyemo, J.: Reservoir inflow forecasting using differential evolution trained neural networks. In: Tantar, A.-A., et al. (eds.) EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, Advances in Intelligent systems and Computing, vol. 288, pp. 307–319. Springer, Switzerland (2014a)
Oyebode, O., Adeyemo, J., Otieno, F.: Monthly streamflow prediction with limited hydro-climatic variables in the upper Mkomazi River, South Africa using genetic programming. Fresenius Environ. Bull. 23(3), 708–719 (2014)
Oyebode, O.K., Adeyemo, J.A.: Genetic programming: principles, applications and opportunities for hydrological modelling. Int. J. Environ. Ecol. Geomatics Earth Sci. Eng. 8(6), 305–311 (2014b)
Pal, S., Qu, B., Das, S., Suganthan, P.: Optimal synthesis of linear antenna arrays with multi-objective differential evolution. Prog. Electromagnet. Res. PIER B 21, 87–111 (2010)
Piotrowski, A.P., Napiorkowski, J.J.: Optimizing neural networks for river flow forecasting Evolutionary Computation methods versus the Levenberg Marquardt approach. J. Hydrol. 407(1), 12–27 (2011)
Price, K., Storn, R.: Differential Evolution (DE) for Continuous Function Approximation (2013). http://www1.icsi.berkeley.edu/~storn/code.html. Accessed 19 July 2013
Qian, G., Zhao, X.: On time series model selection involving many candidate ARMA models. Comput. Stat. Data Anal. 51(12), 6180–6196 (2007)
Siou, L.K.A., Johannet, A., Valrie, B.E., Pistre, S.: Optimization of the generalization capability for rainfall runoff modeling by neural networks: the case of the Lez aquifer (Southern France). Environ. Earth Sci. 65(8), 2365–2375 (2012)
Storn, R., Price, K.: Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Taylor, V., Schulze, R., Jewitt, G.: Application of the indicators of hydrological alteration method to the Mkomazi River, KwaZulu-Natal, South Africa. African J. Aquatic Sci. 28(1), 1–11 (2003)
Zhang, L., Mernyi, E., Grundy, W.M., Young, E.F.: Inference of surface parameters from near-infrared spectra of crystalline H2OH2O ice with Neural Learning. Publ. Astron. Soc. Pacific 122(893), 839–852 (2010)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Adeyemo, J., Oyebode, O., Stretch, D. (2018). River Flow Forecasting Using an Improved Artificial Neural Network. In: Tantar, AA., Tantar, E., Emmerich, M., Legrand, P., Alboaie, L., Luchian, H. (eds) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI. Advances in Intelligent Systems and Computing, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-69710-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-69710-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69708-6
Online ISBN: 978-3-319-69710-9
eBook Packages: EngineeringEngineering (R0)