Efficient dissemination of rainfall forecasting to safeguard farmers from crop failure using optimized neural network model

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Abstract

In the field of weather forecasting, especially in rainfall prediction many researchers employed different data mining techniques. There is numerous method of organizing agricultural engineering substance and it remains an open research issue particularly when taking to distinctive arrangements of clients-farmers, agricultural engineers, agri-organizations-both from proficiency point of view. Keeping these factors Indian farmers in mind, we have chosen to do research on efficient dissemination of rainfall forecasting to safeguard farmers from crop failure using optimized neural network (NN) model. Here, at first, we generate the feature matrix based on five feature indicator. Once the feature matrix is formed, the prediction is done based on the hybrid classifier. In hybrid classifier, particle swarm optimization algorithm is combined with Grey Wolf optimization for training the RBF NN. The performance of the algorithm is analyzed with the help of real datasets gathered from pechiparai and perunchani regions.

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Ananthanarayanan, B., Balan, S., Balamurali, A. M., & Balamurali, K. (2017). Efficient dissemination of rainfall forecasting to safeguard farmers from crop failure using optimized neural network model. International Journal of Intelligent Engineering and Systems, 10(1), 38–47. https://doi.org/10.22266/ijies2017.0228.05

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