Prediction of temperature for various pressure levels using ANN and multiple linear regression techniques: A case study

被引：11

作者：

Jain S. ^{[1
]}

Rathee S. ^{[2
]}

Kumar A. ^{[3
]}

Sambasivam A. ^{[4
]}

Boadh R. ^{[5
]}

Choudhary T. ^{[6
]}

Kumar P. ^{[7
]}

Kumar Singh P. ^{[8
]}

机构：

[1] Department of Mathematics, Amity School of Applied Sciences, Amity University Haryana, Gurugram

[2] Department of Electrical Engineering, DPG Institute of Technology and Management, Haryana, Gurgaon

[3] Department of Mechanical Engineering, Faculty of Engineering & Technology, Shree Guru Gobind Singh Tricentenary University, Gurugram

[4] Department of Mechanical Engineering, Saveetha Engineering College, Thandalam, Tamilnadu, Chennai

[5] School of Basic and Applied Sciences, K R Mangalam University, Haryana, Gurgaon

[6] PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur

[7] Department of Mechanical Engineering, Rawal Institute of Engineering and Technology, Haryana, Faridabad

[8] Department of Mechanical and Automation Engineering, Amity University, Jharkhand, Ranchi

来源：

Materials Today: Proceedings | 2022年 / 56卷

关键词：

Artificial Neural Network; Multiple linear regression; Pressure level; Temperature;

D O I：

10.1016/j.matpr.2022.01.067

中图分类号：

学科分类号：

摘要：

In this paper, the estimation capacities of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) are examined to forecast temperature at two pressure level 400 hPa and 980 hPa approximately over Delhi Safdarjung Airport Station. Different meteorological parameters such as temperature, relative humidity, dew point temperature, mixing ratio, potential temperature has been taken as an input for ANN and MLR models. Statistical bases give us forecasting of temperature by using above mentioned parameters. ANN gives us better results in forecasting temperature as compared to MLR and if compared different techniques of ANN then Multiple layer Artificial Neural Network (MLANN) proved to be best as compared to Single layer Artificial Neural Network (SLANN). © 2022

引用

页码：194 / 199

页数：5

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