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.

中文翻译：

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使用时变粒子群优化 (TVPSO) 和深度学习对降雨数据进行优化预处理

近年来，降雨预报在气象部门发挥了重要作用。降雨量的变化可能会影响世界的制造业和服务业。降雨预测是为天气信息和水文开发应用提供输入数据的重大进展。在机器学习中，准确高效的降雨预测被用来支持流域管理策略。降雨的预测是一个有问题的事件，并且一直是一项具有挑战性的任务。本文实现了一种新算法，用于使用来自各种天气参数集合的历史天气进行预处理和优化。移动平均概率回归过滤 (MV-PRF) 方法从数据库中消除了振幅较小的不需要的样本。时变粒子群优化 (TVPSO) 模型优化了预处理降雨数据。然后这个优化的数据被用于不同的分类过程。预处理方法强调时间序列的近期降雨数据，以使用分类方法改进降雨预报。机器学习 (ML) 技术对天气参数进行分类，以预测每日或每月的降雨量。这些实验结果表明，所提出的方法对于降雨分析是有效和准确的。机器学习 (ML) 技术对天气参数进行分类，以预测每日或每月的降雨量。这些实验结果表明，所提出的方法对于降雨分析是有效和准确的。机器学习 (ML) 技术对天气参数进行分类，以预测每日或每月的降雨量。这些实验结果表明，所提出的方法对于降雨分析是有效和准确的。