Title: Estimation of average monthly rainfall with neighbourhood values: comparative study between soft computing and statistical approach

Authors: Bimal Datta; Susanta Mitra; Srimanta Pal

Addresses: Department of Computer Science and Engineering, Hooghly Engineering and Technology College, Vivekananda Road, Pipulpati, P.O. & Dist. Hooghly, West Bengal, India ' Department of Computer Science and Engineering and IT, Adamas Institute of Technology (RICE Group), Adamas Knowledge City, Barasat Barrackpore Road, Barbaria, P.O. Jagannathpur, Barasat, Kolkata – 700126, India ' Electronics and Communication Sciences Unit, Indian Statistical Institute, B.T. Road, Kolkata – 700108, India

Abstract: In this study, we demonstrate how connectionist models, in particular, multilayer perceptron network can be used for prediction of rainfall. Here we give a comparative study between conventional approach (using multivariate linear regression) and soft computing approach using artificial neural network (ANN). The basic idea is to identify a computational model to characterise the relation between the average monthly rainfalls of a region with that of different neighbouring regions. The model exploits both spatial as well as temporal information to achieve better prediction. Once the computational model is obtained, it is used to predict the average monthly rainfall. Early prediction of rainfall is expected to play a key role in economic planning.

Keywords: rainfall estimation; multivariate linear regression; multilayer perceptron network; artificial neural networks; ANNs; neighbourhood values; statistical approach; soft computing; multilayer perceptron model; MLP; self-organised feature map; SOFM-MLP; feature selection; FS-MLP; precipitation estimation; average monthly rainfall; modelling; rainfall prediction; economic planning.

DOI: 10.1504/IJAISC.2014.065799

International Journal of Artificial Intelligence and Soft Computing, 2014 Vol.4 No.4, pp.302 - 317

Received: 24 Oct 2012
Accepted: 20 Nov 2013

Published online: 29 Nov 2014 *

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