Adeel Tahir, Ahmed Ali Rajput, Mustaqeem Zahid, Shafiq Ur Rehman
{"title":"利用人工智能进行基于三角形的温度测定","authors":"Adeel Tahir, Ahmed Ali Rajput, Mustaqeem Zahid, Shafiq Ur Rehman","doi":"10.1007/s12648-024-03381-3","DOIUrl":null,"url":null,"abstract":"<p>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. <span>\\({\\chi }^{2}\\)</span>, and coefficient of determination (R<sup>2</sup>). 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.</p>","PeriodicalId":584,"journal":{"name":"Indian Journal of Physics","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Triangular based determination of temperature using artificial intelligence\",\"authors\":\"Adeel Tahir, Ahmed Ali Rajput, Mustaqeem Zahid, Shafiq Ur Rehman\",\"doi\":\"10.1007/s12648-024-03381-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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. <span>\\\\({\\\\chi }^{2}\\\\)</span>, and coefficient of determination (R<sup>2</sup>). 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.</p>\",\"PeriodicalId\":584,\"journal\":{\"name\":\"Indian Journal of Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Indian Journal of Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1007/s12648-024-03381-3\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indian Journal of Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1007/s12648-024-03381-3","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.