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

N.Vivekanandan

doi:10.21276/ijircst.2017.5.4.5

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.

Keywords

Artificial Neural Network, Correlation Coefficient, Extreme Value Type-1, Mean Absolute Percentage Error, Model Efficiency, Multi Layer Perceptron

References

[1] 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.

[2] 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.

[3] 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

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

[5] 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

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

[7] 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.

[8] 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.

[9] 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

[10] E. Vamsidhar, K.V.S.R.P. Varma, P. Sankara Rao, and R. Satapati, “Prediction of rainfall using back propagation neural network model”, InternationaJournal on Computer Science and Engineering, vol. 2, no. 4, 2010, pp. 1119-1121.

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] 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.

Cites this article as

N.Vivekanandan "Evaluation of Performance of Statistical and ANN Approaches for Prediction of Rainfall", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-5, Issue-4, Page No-323-327, 2017. Available from: https://doi.org/10.21276/ijircst.2017.5.4.5

Corresponding Author

N.Vivekanandan

Scientist-B, Central Water and Power Research Station, Pune 411024, Maharashtra, India, Phone: +91 20 2410 3367.

Download Full Paper

Download PDF

No. of Downloads: 11 | No. of Views: 1141

A Comparative Study of ChatGPT, Gemini, and Perplexity

Manali Shukla, Ishika Goyal, Bhavya Gupta, Jhanvi Sharma.

July 2024 - Vol 12, Issue 4
Helmet Detection and Number Plate Recognition Using YOLOv8 and Tensorflow Algorithm in Machine Learning

Dipti Prajapati, Samishtarani Sabat, Sanika Bhilare, Rashmi Vishe, Prof. Suman Bhujbal.

March 2024 - Vol 12, Issue 2
Machine Learning Prospects: Insights for Social Media Data Mining and Analytics

Anu Sharma, Vivek Kumar.

May 2023 - Vol 11, Issue 3

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

Citations

Download Full Paper PDF

Total View 1141

Total Download 11