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An efficient approach in rainfall prediction around Sathanur Dam, India, by model based clustering, structural equation modelling (SEM) and artificial neural networks (ANN)

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Abstract

In the present paper, the rainfall forecast information is analyzed using model and density based clustering algorithms and good model is fitted by using structural equation modelling and artificial neural networks. Shapiro-Wilk normality test for Water Level, In Flow, Period, Rainfall are 0.7694, 0.2388, 0.7651, 0.4382 respectively with same p-value \(< 2.2 \times 10^{-16}\). It shows that all are highly significantly non-normal. For the percentage on the Water Level, the variances were similar for Yes and No (Rainfall), significantly different, but for inflow the variances were not significantly different in the two groups, whereas for the period, the variances were similar for Yes and No (Rainfall), significantly different. The number of observations in the clusters 0 to cluster 9 are 18, 64, 39, 20, 19, 88, 41, 41, and 36. Here, cluster 0 represents the outlier in which we have 19 observations out of 366 observations in model based clustering. Furthermore, DBSCAN performs better for these data sets and can identify the correct set of clusters compared to k-means algorithms. The Fit indices for our structural equation model in which most of them show that our model is good to fit except the absolute type with Chi-square \(\left( \chi ^2\right) \). Finally, we use 10 fold cross validated MSE for the linear model. The cv.glm is 0.1252 and it is shown in the box plot.

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Acknowledgements

This study was supported by the Department of Science and Technology, Fund for Improvement of S &T infrastructure in universities & higher educational institutions (FIST), Government of India(Reference number: SR/FST/MSI-107/ 2015(C)). The authors also thank DST(FIST) for providing financial support and Prof. S. Surianarayanan, School of Civil Engineering, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam, for providing the data set to enhance the quality of the paper.

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  1. Department of Mathematics, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam, Tamil Nadu, 612001, India

    K. Kannan

  2. Department of Computer Science and Engineering, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam, Tamil Nadu, 612001, India

    A. Menaga

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Kannan, K., Menaga, A. An efficient approach in rainfall prediction around Sathanur Dam, India, by model based clustering, structural equation modelling (SEM) and artificial neural networks (ANN). Afr. Mat. 33, 89 (2022). https://doi.org/10.1007/s13370-022-01023-7

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