Machine Language Approach for Modeling and Predicting Rainfall in Different Zones of Kerala
88 / 97
Authors
-
Gokul Krishnan K.B
Acharya Narendra Deva University of Agriculture and Technology , Ayodhya
-
Vishal Mehta
Acharya Narendra Deva University of Agriculture and Technology , Ayodhya
-
V.N. Rai
Acharya Narendra Deva University of Agriculture and Technology , Ayodhya
Keywords:
ANN; ELM; MLP; Northern zone; Central zone; Southern zone.Abstract
The forecasting of rainfall is said to be the most difficult of other hydrological processes due to sudden changes in atmospheric processes. The rainfall directly and indirectly influences agriculture and allied sectors. Sudden changes in rainfall or an uneven distribution of rainfall can lead to crop loss. In order to avoid such problems and take the necessary precautions, it is mandatory to forecast the rainfall using various models with maximum precision. In this study, rainfall for northern, central and southern Kerala, India, was predicted using an artificial neural network (ANN) with a multi-layer perceptron (MLP) feed-forward neural network and an extreme learning machine (ELM) neural network. The monthly rainfall data was collected for a period of 39 years (1982–2020) from the regional agricultural research stations (RARS), Pilicode and Pattambi, for the northern and central zones of Kerala, respectively, whereas for the southern zone of Kerala, data was collected from RARS, Vellayani, for a period of 36 years (1985–2020). For the rainfall data collected from three different zones of Kerala, the MLP and ELM were applied. The comparison and validation of MLP and ELM models was done based on the error values of mean square error (MSE), root mean square error (RMSE), and mean absolute error
(MAE). The results indicated that for three different zones in Kerala, ANN with MLP showed better performance in forecasting rainfall compared to the ELM model. The best-selected model was also used for forecasting the next 5 years rainfall in each zone of Kerala.
Downloads
References
Anctil, F., Perrin, C. and Andréassian, V. (2004). Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models. Environmental Modelling & Software, 19(4), 357-368. https://pdf.sciencedirectassets.com/271872/1-s2.0-S1364815200X00485/1s2.0-S136481520300135X/main.pdf
Bodri, L. and Cermak, V. (2000). Prediction of Extreme Precipitation Using a Neural Network: Application to Summer Flood Occurrence in Moravia. Advances in Engineering Software, 31(5), 311–321. https://pdf.sciencedirectassets.com/271418/1-s2.0S0965997800X00439/1-s2.0-S0965997899000630/main.pdf
Cholissodin, I. and Sutrisno, S. (2018). Prediction of rainfall using simplified deep learning based extreme learning machines. Journal of Information Technology and Computer Science, 3(2), 120-131. https://jitecs.ub.ac.id/index.php/jitecs/article/view/58
Crone, S.F. and Kourentzes, N. (2010). Feature selection for time series prediction – A combined filter and wrapper approach for neural networks. Neurocomputing, 73(10-12), 1923-1936. https://pdf.sciencedirectassets.com/271597/1-s2.0-S0925231210X00042/1s2.0-S0925231210000974/main.pdf
Dash, Y., Mishra, S.K. and Panigrahi, B.K. (2017). Rainfall prediction of a maritime state (Kerala), India using SLFN and ELM techniques. In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE. 1714-1718. https://ieeexplore.ieee.org/abstract/document/8342829
Dash, Y., Mishra, S.K. and Panigrahi, B.K. (2018). Rainfall prediction for the Kerala state of India using artificial intelligence approaches. Computers & Electrical Engineering, 70, 66-73. https://pdf.sciencedirectassets.com/271419/1-s2.0-S0045790618X00068/1s2.0-S004579061732791X/main.pdf
Deo, R.C., Tiwari, M.K., Adamowski, J.F. and Quilty, J.M. (2017). Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stochastic Environmental Research and Risk Assessment, 31, 1211-1240. https://link.springer.com/content/pdf/10.1007/s00477-016-1265-z.pdf
El-Shafie, A.H., El-Shafie, A., El Mazoghi, H.G., Shehata, A. and Taha, M.R. (2011). Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt. International Journal of Physical Sciences, 6(6), 1306-1316. https://academicjournals.org/journal/IJPS/article-full-text-pdf/A8043DC28734.pdf
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. 2nd ed. Prentice-Hall, New Jersey. https://cdn.preterhuman.net/texts/science_and_technology/artificial_intelligence/Neural%20Networks%20-%20A%20Comprehensive%20Foundation%20-%20Simon%20Haykin.pdf
Huang, G.B., Zhu, Q.Y. and Siew, C.K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1-3), 489-501. https://www.sciencedirect.com/science/article/abs/pii/S0925231206000385
Hung, N.Q., Babel, M.S., Weesakul, S. and Tripathi, N.K. (2009). An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences, 13(8), 1413-1425. https://hess.copernicus.org/articles/13/1413/2009/
Islam, T., Saha, S., Evan, A.A., Halder, N. and Dey, S.C. (2016). Monthly Weather Forecasting through ANN Model: A Case Study in Barisal, Bangladesh. International Journal of Advanced Research in Computer and Communication Engineering, 5(6), 1-6. https://ijarcce.com/upload/2016/june-16/IJARCCE%201.pdf
Kourentzes, N., Barrow, B.K. and Crone, S.F. (2014). Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244. https://www.sciencedirect.com/science/article/abs/pii/S0957417413009834
Krishnan, G.K.B., Mehta, V. and Yadav, R.S. (2022). Assessment of future pattern of rainfall in different zones of Kerala using incorporation of SARIMA, ANN and Hybrid SARIMA-ANN models. Economics Affairs, 67(05), 823-832. https://ndpublisher.in/admin/issues/EAv67n5q.pdf
Krishnan, G.K.B., Mehta, V. and Rai, V.N. (2023). Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala. MAUSAM, 74(4), 1053-1064. https://mausamjournal.imd.gov.in/index.php/MAUSAM/article/view/5603
Lippmann, R. (1987). An introduction to computing with neural nets. IEEE ASSP Mag, 4(2), 4-22. https://ieeexplore.ieee.org/abstract/document/1165576
Mukaram, M.Z. and Yusof, F. (2017). Solar radiation forecast using hybrid SARIMA and ANN model: A case study at several locations in Peninsular Malaysia. Malaysian Journal of Fundamental and Applied Sciences, Special Issue on Some Advances in Industrial and Applied Mathematics, 346-350. https://mjfas.utm.my/index.php/mjfas/article/view/895/pdf
Nalcaci, G., Özmen, A. and Weber, G.W. (2019). Long-term load forecasting: models based on MARS, ANN and LR methods. Central European Journal of Operations Research, 27, 1033-1049. https://link.springer.com/content/pdf/10.1007/s10100-018-0531-1.pdf
Ord, K., Fildes, R. and Kourentzes, N. (2017). Principles of Business Forecasting (2nd ed.). Wessex Press Publishing Co., Chapter 10. https://wessexlearning.com/products/principles-of-business-forecasting2nd-ed
Özmen, A. and Weber, G.W. (2014). RMARS: robustification of multivariate adaptive regression spline under polyhedral uncertainty. Journal of Computer and Applied Mathematics, 259(B), 914–924. https://www.sciencedirect.com/science/article/pii/S0377042713005104
Pandey, P.K., Tripura, H. and Pandey, V. (2019). Improving prediction accuracy of rainfall time series by Hybrid SARIMA–GARCH modeling. Natural Resources Research, 28, 1125-1138. https://link.springer.com/article/10.1007/s11053-018-9442-z
Panigrahi, S. and Behera, H.S. (2017). A hybrid ETS–ANN model for time series forecasting. Engineering Applications of Artificial Intelligence, 66, 49-59. https://www.sciencedirect.com/science/article/abs/pii/S0952197617301550
Paul, R.K., Prajneshu and Ghosh, H. (2013). Wavelet Frequency Domain Approach for Modelling and Forecasting of Indian Monsoon Rainfall Time-Series Data. Journal of The Indian Society of Agricultural Statistics, 67(3), 319-327. http://www.isas.org.in/jisas/volume/vol67/issue3/4-RanjitKumar.pdf
Paul, R.K., Ghosh, H. and Prajneshu (2014). Development of out-of-sample forecast formulae for ARIMAX-GARCH model and their application. Journal of The Indian Society of Agricultural Statistics, 68(1), 85-92. http://www.isas.org.in/jisas/volume/vol68/issue1/08-RanjitKumar.pdf
Ramirez, M.C.V., de Campos Velho, H.F. and Ferreira, N.J. (2005). Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. Journal of Hydrology, 301(1-4), 146-162. https://www.sciencedirect.com/science/article/abs/pii/S0022169404003191
Rosenblatt, F. (1962). Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Spartan Books, pp 666. https://safari.ethz.ch/digitaltechnik/spring2018/lib/exe/fetch.php?media=neurodynamics1962rosenblatt.pdf
Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536. https://www.nature.com/articles/323533a0
Sahai, A.K., Somann, M.K. and Satyan, V. (2000). All India Summer Monsoon Rainfall Prediction Using an Artificial Neural Network. Climate Dynamics, 16, 291-302. https://link.springer.com/content/pdf/10.1007/s003820050328.pdf
Shukla, A., Kumar, S. and Singh, H. (2021). MLP-ANN-Based execution time prediction model and assessment of input parameters through structural modeling. Proceedings of the National Academy of Sciences, India Section A: Physical Science, 91, 577-585. https://link.springer.com/article/10.1007/s40010-020-00695-9
Tripathy, A.K., Mohapatra, S., Beura, S. and Pradhan, G. (2011). Weather forecasting using ANN and PSO. International Journal of Scientific Engineering and Research, 2(7), 1-5. https://www.ijser.org/viewPaperDetail.aspx?JUL1116
Venkatesh, K. and Bind, Y.K. (2022). ANN and neuro-fuzzy modeling for shear strength characterization of soils. Proceedings of the National Academy of Sciences, India Section A: Physical Science, 92, 243-249. https://link.springer.com/article/10.1007/s40010-020-00709-6
Waciko, K.J. and Ismail, B. (2020). SARIMA-ELM hybrid model versus SARIMA-MLP hybrid model. International Journal of Statistics and Applied Mathematics, 5(2), 01-08. https://www.mathsjournal.com/pdf/2020/vol5issue2/PartA/5-1-13-832.pdf
Wang, J., Hu, J., Ma, K. and Zhang, Y. (2015). A self-adaptive hybrid approach for wind speed forecasting. Renewable Energy, 78, 374-385. https://www.sciencedirect.com/science/article/abs/pii/S0960148115000336
Yaseen, Z.M., Ali, M., Sharafati, A., Al-Ansari, N. and Shahid, S. (2021). Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh. Scientific Reports, 11, 3435. https://www.nature.com/articles/s41598-021-82977-9.pdf
Downloads
Submitted
Published
Issue
Section
How to Cite
