About 70% of the economic sector of India depends on agriculture. Rainfall prediction plays an important role in fields like agricultural sector, fisheries, aviation, irrigation etc. Typically, in Anand (Gujarat) India, the advent of monsoon starts from June month and it continues till September month. In this work, multilayered neural network with Back-propagation learning algorithm is used. We have configured Feed forward and cascade network with 1000 epoch and achieved 82% and 81% accuracy respectively. Data-readings of various factors from June to September have been taken. One by one each factor is tested for accuracy without its presence with the help of neural network. As per our analysis, temperature, relative humidity and vapor pressure are important factors to predict rainfall.
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Rainfall Prediction using Neural Network
Semantic Scholar · Environmental Science · 2018
Abstract
About 70% of the economic sector of India depends on agriculture. Rainfall prediction plays an important role in fields like agricultural sector, fisheries, aviation, irrigation etc. Typically, in Anand (Gujarat) India, the advent of monsoon starts from June month and it continues till September month. In this work, multilayered neural network with Back-propagation learning algorithm is used. We have configured Feed forward and cascade network with 1000 epoch and achieved 82% and 81% accuracy respectively. Data-readings of various factors from June to September have been taken. One by one each factor is tested for accuracy without its presence with the help of neural network. As per our analysis, temperature, relative humidity and vapor pressure are important factors to predict rainfall.