International Journal For Multidisciplinary Research

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A fine-grained, Multi-model system to Predict Rainfall across diverse datasets

Author(s) Shubham Gade, Alfiya Shahbad, Pradnya Randive, Chanchal Vakte
Country United States
Abstract Rainfall is crucial to agriculture and a farmer's means of subsistence. It has been very helpful to farmers,
especially in the agricultural society of the Indian Subcontinent, where it is heavily used for agricultural
production, ground water replenishment, and irrigation. Rainfall is therefore a vital and important event
for a sizable section of the Indian population. Therefore, rainfall prediction is by far the most effective
and advantageous method that has the power to drastically improve the lives of a great number of
people. The process of estimating the probability of rainfall at a given place, forecasting future
precipitation, and figuring out how much rain will fall in particular locations is known as rainfall
prediction. Along with the probability of rainfall at that specific place, it takes into account the
precipitation volume assessment, forecast accuracy, and prediction error. Consequently, an efficient
method for rainfall prediction is presented in this research effort. The proposed method uses Artificial
neural network (ANN) and Long short term memory (LSTM) model to estimate the rainfall prediction.
This research used many datasets from the Indian subcontinent and other continent-wide datasets to
estimate the rainfall. Results are measured for the Root mean square error (RMSE) parameter on a
variety of datasets, demonstrating how well the system performs in comparison to some previous efforts.
Keywords Rainfall prediction, Deep Learning models, Artificial Neural network, long short-term memory
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-24
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.33767
Short DOI https://doi.org/g8w2wh
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