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Optimized Preprocessing using Time Variant Particle Swarm Optimization (TVPSO) and Deep Learning on Rainfall Data

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Title Optimized Preprocessing using Time Variant Particle Swarm Optimization (TVPSO) and Deep Learning on Rainfall Data
 
Creator P, Umamaheswari
Ramaswamy, V
 
Subject Classification
Machine learning
Optimization
Rainfall prediction
Time series
 
Description 1317-1325
In the recent past, rainfall prediction has played a significant role in the meteorology department. Changes in rainfall
might affect the world's manufacturing and service sectors. Rainfall prediction is a substantial progression in giving
input data for weather information and hydrological development applications. In machine learning, accurate and
efficient rainfall predictionis used to support strategy for watershed management. The prediction of rain is a problematic
occurrence and endures to be a challenging task. This paper implements a novel algorithm for preprocessing and
optimization using historical weather from a collection of various weather parameters. The Moving Average-Probabilistic
Regression Filtering (MV-PRF) method eliminates unwanted samples with less amplitude from the database. The Time
Variant Particle Swarm Optimization (TVPSO) model optimizes the preprocessing rainfall data. Then this optimized data is
used for the different classification processes. The preprocessing methods emphasize the recent rainfall data of the time
series to improve the rainfall forecast using classification methods. Machine Learning (ML) technique classifies the weather
parameters to predict rainfall daily or monthly. These experimental results show that the proposed methods are efficient and
accurate for rainfall analysis.
 
Date 2022-12-07T11:33:33Z
2022-12-07T11:33:33Z
2022-12
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61001
https://doi.org/10.56042/jsir.v81i12.69310
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.81(12) [December 2022]