Application Research of Rainfall Prediction Based on Optimized Machine Learning Algorithm in Meteorological Data
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초록
In recent years, the rapid development of artificial intelligence technology has brought new opportunities to themeteorological field. Specifically, machine learning (ML) algorithms have proven valuable tools in rainfallretrievals, demonstrating the practicability of using ML algorithms when facing high-dimensional and complexdata. By collecting data and using ML algorithms to mine and analyze the data, ML models can solve theproblem of rainfall prediction in meteorology. Spurred by this advantage, this paper compared five MLalgorithms for rainfall prediction using the National Population Health Science data from China, and the fiveML algorithms were optimized appropriately. The data employed was first preprocessed to find and fill in themissing values, remove duplicate values, mine the correlation between data features, and generate visual results. Then, logistic regression, k-nearest neighbor algorithm, naive Bayes, decision tree algorithms, and randomforest were used to mine and analyze the meteorological data for weather prediction. Finally, the performanceof the models before and after optimization is compared to provide decision support for rainfall prediction.
Abstract
In recent years, the rapid development of artificial intelligence technology has brought new opportunities to themeteorological field. Specifically, machine learning (ML) algorithms have proven valuable tools in rainfallretrievals, demonstrating the practicability of using ML algorithms when facing high-dimensional and complexdata. By collecting data and using ML algorithms to mine and analyze the data, ML models can solve theproblem of rainfall prediction in meteorology. Spurred by this advantage, this paper compared five MLalgorithms for rainfall prediction using the National Population Health Science data from China, and the fiveML algorithms were optimized appropriately. The data employed was first preprocessed to find and fill in themissing values, remove duplicate values, mine the correlation between data features, and generate visual results. Then, logistic regression, k-nearest neighbor algorithm, naive Bayes, decision tree algorithms, and randomforest were used to mine and analyze the meteorological data for weather prediction. Finally, the performanceof the models before and after optimization is compared to provide decision support for rainfall prediction.
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