用于智能降雨预测模型的优化级联CNN:基于统计的机器学习研究

Mobin Akhtar, A. Shatat, Shabir Ahamad, Sara Dilshad, Faizan Samdani
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引用次数: 0

摘要

根据先前的研究,使用人工智能来预测天气状况可以提供积极的结果。气象时间序列预报可以帮助防灾人员做出更明智的判断。深度学习最近被证明是解决复杂问题和分析大量数据的可行技术。统计学习理论是一种结合了统计学和功能分析的机器学习。为了回答降雨预报的问题,本研究采用了基于统计的机器学习技术。首先对基准气象数据进行数据增强和数据归一化预处理。然后,机器学习被赋予诸如“一阶和二阶统计信息”之类的统计特征来进行预测。采用基于自适应搜索缩放因子的大象放牧优化(ASS-EHO)方法对级联卷积神经网络(CNN)进行降雨预测优化,作为一种改进的预测模型,对级联CNN计数、隐藏神经元计数、激活函数等参数进行优化。新的预测模型是一种基于统计的机器学习模型,其目标函数是交叉熵损失函数的约简。结果与已建立的统计方法进行了比较,表明该模型可用于快速准确地估计日降雨量。
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Optimized cascaded CNN for intelligent rainfall prediction model: a research towards statistic-based machine learning
Abstract Using artificial intelligence to anticipate weather conditions, according to prior research, can provide positive results. Forecasts of meteorological time series can aid disaster-prevention personnel in making more informed judgments. Deep learning has recently been shown to be a viable technique for solving complicated issues and analyzing large amounts of data. Statistical learning theory is a type of machine learning that combines statistics and functional analysis. To answer the problem of rainfall forecasting, this study employs a statistically-based machine learning technique. The benchmark meteorological data is first pre-processed using data augmentation and data normalization. The machine learning is then given statistical characteristics such as "first order and second order statistical information" for prediction. The Adaptive Searched Scaling factor-based Elephant Herding Optimization (ASS-EHO)is used to optimize the Cascaded Convolutional Neural Network (CNN) for rainfall prediction as an improved prediction model, with parameter tuning such as cascaded CNN count, hidden neuron count, and activation function optimized. The new prediction model is a statistical-based machine learning model in which the aim function is the reduction of the cross entropy loss function. The results are compared to established statistical methodologies, demonstrating that the model may be used to estimate daily rainfall quickly and accurately.
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来源期刊
CiteScore
4.10
自引率
6.20%
发文量
38
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