Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall

Authors

  • N Vivekanandan Scientist-B, Central Water and Power Research Station, Pune 411024, Maharashtra, India Author

Keywords:

Artificial Neural Network, Model Efficiency, Multi Layer Perceptron

Abstract

Prediction of rainfall for a river basin is of  utmost importance for planning and design of irrigation and  drainage systems as also for command area development.  Since the distribution of rainfall varies over space and time, it  is required to analyze the data covering long periods and  recorded at various locations to arrive at reliable information  for decision support. Further, such data need to be analyzed  in different ways, depending on the issue under  consideration. In the present study, Extreme Value Type-1  (EV1) distribution based on statistical approach and Multi  Layer Perceptron (MLP) network based on Artificial Neural  Network (ANN) is adopted for prediction of rainfall at  Fatehabad and Hansi. The performance of the statistical and  ANN approaches used in rainfall predication are evaluated by  model performance indicators viz., correlation coefficient, model  efficiency and mean absolute percentage error. The study shows  the MLP is found to be better suited network for prediction of  rainfall at Fatehabad whereas EV1 for Hansi.  

Downloads

Download data is not yet available.

References

V.K. Somvanshi, O.P. Pandey, P.K. Agrawal, N.V. Kalanker, M. Ravi Prakash, and Ramesh Chand, “Modelling and prediction of rainfall using artificial

neural network and ARIMA techniques”, Journal of Indian and Geophysical Union, vol. 10, no. 2, 2006, pp. 141-151.

X.K. Wang, W.Z. Lu, S.Y. Cao, and D. Fang, “Using time delay neural network combined with genetic algorithm to predict runoff level of Linsham watershed, Sinchuan, China”, Journal of Hydrologic Engineering, vol. 12, no. 2, 2007, pp. 231-236.

K.P. Sudheer, K. Srinivasan, T.R. Neelakantan, and V. Srinivas, “A nonlinear data-driven model for synthetic generation of annual streamflows”, Hydrological Processes, vol. 22, no. 12, 2008, pp. 1831-1845

E.J. Gumbel, “Statistic of Extremes”, Columbia Univ. Press, New York, 1960.

S. Tokar, and M. Markus, “Precipitation runoff modelling using artificial neural network and conceptual models”, Journal of Hydrologic Engineering, vol. 5, no. 2, 2000, pp. 156-161

M. Kaltech, “Rainfall-runoff modelling using artificial neural networks: modelling and understanding”, Journal of Environmental Sciences, vol. 6, no. 1, 2008, pp. 53-58.

M.P. Rajurkar, U.C. Kothyari, and U.C. Chaube, “Modelling of the daily rainfall- runoff relationship with artificial neural network”, Journal of Hydrology, vol. 285, nos. 1-4, 2004, pp. 96-113.

C. Sarangi, A. Madramootoo, P. Enright, S.O. Prashar, and R.M. Patel, “Performance evaluation of ANN and geomorphology-based models for runoff and sediment yield prediction for a Canadian watershed”, Current Science, vol. 89, no. 12, 2005, pp. 2022-2033.

L.M. See, A. Jain, R.J. Abrahart, and C.W. Dawson, “Visualization of hidden neuron behaviour in a neural network rainfall-runoff model, Practical Hydroinformatics: Computational intelligence and technological developments in water applications”, Springer-Verlag, 2008, pp. 87-99

E. Vamsidhar, K.V.S.R.P. Varma, P. Sankara Rao, and R. Satapati, “Prediction of rainfall using back propagation neural network model”, International

Journal on Computer Science and Engineering, vol. 2, no. 4, 2010, pp. 1119-1121.

H.E. Amr, A. El-Shafie, G.E. Hasan, A. Shehata, and M.R. Taha, “Artificial neural network technique for rainfall forecasting applied to Alexandria”, International Journal of the Physical Sciences, vol. 6, no. 6, 2011, pp. 1306-1316.

R.R. Deshpandey, “On the rainfall time series prediction using multilayer perceptron artificial neural network”, International Journal of emerging technology and advanced engineering, vol. 2, no. 1, 2012, pp. 2250-2459.

N. Vivekanandan, “Comparison of MLP, CCL and CGT networks for prediction of rainfall”, International Journal of Computer and Communication Engineering Research, vol. 1, no. 2, 2014, pp. 36-40.

L. Ma, S.Y. Luan, C.W. Jiang, H.L. Liu, and Y. Zhang, “A Review on the forecasting of wind speed and generated power”, Renewable and Sustainable Energy Reviews, vol. 13, no. 4, 2009, pp. 915-920.

Chen, and B.J. Adams, “Integration of artificial neural networks with conceptual models in rainfall-runoff modelling”, Journal of Hydrology, vol. 318, no. 1-4, 2006, pp. 232-249.

Downloads

Published

2017-07-05

Issue

Vol. 5 No. 4 (2017): International Journal of Innovative Research in Computer Science & Technology(july)2017

Section

Articles

How to Cite

Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall . (2017). International Journal of Innovative Research in Computer Science & Technology, 5(4), 323–327. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13470