利用人工智能进行基于三角形的温度测定

IF 1.6 4区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Adeel Tahir, Ahmed Ali Rajput, Mustaqeem Zahid, Shafiq Ur Rehman
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引用次数: 0

摘要

预报方法出现于二十世纪中叶,其应用在生活的方方面面呈指数级增长。更重要的是,估算现代气象参数有助于在天气、健康和农业安全措施方面做出正确决策。同样,本研究旨在利用奎达(巴基斯坦)的三个邻近站点 Chaman、Kalat 和 Sibi,找到一种更好的拟合技术来翻译奎达的气温分布。为此,本研究采用了一种名为人工神经网络的著名机器学习技术。此外,还考虑了四种训练算法,以优化模型性能。除此之外,还采用了另一种传统的统计模型,即多元线性回归模型(MLR)。由于温度分布具有非线性趋势,因此多重线性回归技术也可用于预测。机器学习和线性统计模型提供了从 2011 年到 2017 年的七年数据,用于训练。为 2018 年、2019 年和 2020 年的三组数据提供数据,以确定这些训练有素的模型与实际气温分布的接近程度。为评估模型性能,对不同误差进行了评估,如平均平方误差(MSE)、平均绝对百分比误差(MAPE)、平均绝对偏差误差(MABE)和卡方误差。\({\chi}^{2}\)和决定系数(R2)。对于 ANN,MABE 和 MAPE 值最低的模型是 ANN-RB 和 ANN-BR,而 MSE 值最低(1.3604)的模型是 ANN-BFG 模型。相关性最高的模型是 ANN-BFG 模型。另一方面,MLR 的 MSE 值为 1.4253,决定系数为 0.9860。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Triangular based determination of temperature using artificial intelligence

A Triangular based determination of temperature using artificial intelligence

The forecasting method emerged in the middle of the twentieth century; its usage has grown exponentially in all aspects of life. More importantly, estimating modern meteorological parameters helps make good decisions regarding weather, health, and agricultural safety measures. Similarly, this study aims to find a better-fitting technique to translate Quetta’s (Pakistan) temperature distribution using its three neighboring stations, Chaman, Kalat, and Sibi. In this regard, a well-known machine learning technique named Artificial Neural Network was utilized. Additionally, four training algorithms are also considered to optimize the model performance. Apart from that, another traditional statistical model is incorporated, which is a Multiple Linear Regression (MLR). Since the temperature distribution has a nonlinear trend, MLR techniques are also useful for making predictions. Machine learning and linear statistical models are provided with seven years of data from 2011 to 2017 for training purposes. Three sets of data for 2018, 2019, and 2020 are fed to determine how these trained models show close agreements with the actual temperature distribution. Different errors are evaluated to assess model performance, such as mean squared error (MSE), mean absolute percentage error (MAPE), mean absolute bias error (MABE), and chi-squared error. \({\chi }^{2}\), and coefficient of determination (R2). For ANN, the models with the lowest MABE and MAPE values are ANN-RB and ANN-BR, whereas the model with the lowest MSE value, 1.3604, is the ANN-BFG model. The model with the highest correlation is the ANN-BFG model. On the other hand, MLR has an MSE of 1.4253 and a coefficient of determination of 0.9860.

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来源期刊
Indian Journal of Physics
Indian Journal of Physics 物理-物理:综合
CiteScore
3.40
自引率
10.00%
发文量
275
审稿时长
3-8 weeks
期刊介绍: Indian Journal of Physics is a monthly research journal in English published by the Indian Association for the Cultivation of Sciences in collaboration with the Indian Physical Society. The journal publishes refereed papers covering current research in Physics in the following category: Astrophysics, Atmospheric and Space physics; Atomic & Molecular Physics; Biophysics; Condensed Matter & Materials Physics; General & Interdisciplinary Physics; Nonlinear dynamics & Complex Systems; Nuclear Physics; Optics and Spectroscopy; Particle Physics; Plasma Physics; Relativity & Cosmology; Statistical Physics.
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