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

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Title Statement <p>Optimized Preprocessing using Time Variant Particle Swarm Optimization (TVPSO) and Deep Learning on Rainfall Data</p>
 
Added Entry - Uncontrolled Name Umamaheswari, P ; Department of Computer Science and Engineering, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam 612 001, Tamil Nadu, India
Ramaswamy, V ; Department of Computer Science and Engineering, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam 612 001, Tamil Nadu, India
 
Uncontrolled Index Term Classification, Machine learning, Optimization, Rainfall prediction, Time series
 
Summary, etc. <p>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.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2022-12-15 12:44:42
 
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http://op.niscair.res.in/index.php/JSIR/article/view/69310
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 81, ##issue.no## 12 (2022): Journal of Scientific & Industrial Research
 
Language Note en