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Machine Language Approach for Modeling and Predicting Rainfall in Different Zones of Kerala

Indian Agricultural Research Journals

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Title Machine Language Approach for Modeling and Predicting Rainfall in Different Zones of Kerala
 
Creator K.B, Gokul Krishnan
Mehta, Vishal
Rai, V.N
 
Subject ANN; ELM; MLP; Northern zone; Central zone; Southern zone.
 
Description 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.
 
Publisher Indian Society of Agricultural Statistics (ISAS)
 
Date 2024-09-10
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://epubs.icar.org.in/index.php/JISAS/article/view/155791
10.56093/jisas.v78i2.2
 
Source Journal of the Indian Society of Agricultural Statistics; Vol. 78 No. 2 (2024); 89-96
0019-6363
 
Language eng
 
Relation https://epubs.icar.org.in/index.php/JISAS/article/view/155791/55603
 
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