用人工神经网络和回归分析确定信德省罗赫里地区的温度分布

Q4 Physics and Astronomy
Adeel Tahir, Muhammad Ashraf, Zaheer Uddin, Muhammad Sarim, Syed Masood Raza
{"title":"用人工神经网络和回归分析确定信德省罗赫里地区的温度分布","authors":"Adeel Tahir, Muhammad Ashraf, Zaheer Uddin, Muhammad Sarim, Syed Masood Raza","doi":"10.53560/ppasa(59-4)654","DOIUrl":null,"url":null,"abstract":"As time passes, the world is facing the problem of global warming, which results in a rise in average daily temperature. Proper knowledge of temperature distribution and future prediction may help to cope with the situation in the near future. Climate forecasting has gone through various faces; in the early days’ people used to predict the behavior qualitatively. Now environmental scientists have developed a quantitative method for forest climate behavior with certain uncertainties. Empirical models have been developed based on regression analysis to estimate temperature distribution. Two models, linear and non linear, use dew point temperature and relative humidity as independent variables. In addition to regression analysis, Artificial Neural Network (ANN) has been utilized to predict the average daily temperatures of Rohri Sindh, a city in Pakistan in the Sindh province. Both empirical models and ANN estimates are in good agreement with the known values of average daily temperatures.","PeriodicalId":36961,"journal":{"name":"Proceedings of the Pakistan Academy of Sciences: Part A","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determination of Temperature Distribution of Rohri, Sindh using Artificial Neural Network and Regression Analysis\",\"authors\":\"Adeel Tahir, Muhammad Ashraf, Zaheer Uddin, Muhammad Sarim, Syed Masood Raza\",\"doi\":\"10.53560/ppasa(59-4)654\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As time passes, the world is facing the problem of global warming, which results in a rise in average daily temperature. Proper knowledge of temperature distribution and future prediction may help to cope with the situation in the near future. Climate forecasting has gone through various faces; in the early days’ people used to predict the behavior qualitatively. Now environmental scientists have developed a quantitative method for forest climate behavior with certain uncertainties. Empirical models have been developed based on regression analysis to estimate temperature distribution. Two models, linear and non linear, use dew point temperature and relative humidity as independent variables. In addition to regression analysis, Artificial Neural Network (ANN) has been utilized to predict the average daily temperatures of Rohri Sindh, a city in Pakistan in the Sindh province. Both empirical models and ANN estimates are in good agreement with the known values of average daily temperatures.\",\"PeriodicalId\":36961,\"journal\":{\"name\":\"Proceedings of the Pakistan Academy of Sciences: Part A\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Pakistan Academy of Sciences: Part A\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53560/ppasa(59-4)654\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Physics and Astronomy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Pakistan Academy of Sciences: Part A","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53560/ppasa(59-4)654","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Physics and Astronomy","Score":null,"Total":0}
引用次数: 0

摘要

随着时间的推移,世界正面临着全球变暖的问题,这导致了平均每日气温的上升。对温度分布和未来预测的正确认识可能有助于在不久的将来应对这种情况。气候预报经历了各种各样的面貌;在早期,人们习惯于定性地预测行为。目前,环境科学家已经开发出一种具有一定不确定性的森林气候行为的定量方法。在回归分析的基础上建立了经验模型来估计温度分布。两种模型,线性和非线性,使用露点温度和相对湿度作为自变量。在回归分析的基础上,利用人工神经网络(ANN)预测了巴基斯坦信德省罗赫里信德市的日平均气温。经验模型和人工神经网络估算值都与已知的日平均气温值很好地吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determination of Temperature Distribution of Rohri, Sindh using Artificial Neural Network and Regression Analysis
As time passes, the world is facing the problem of global warming, which results in a rise in average daily temperature. Proper knowledge of temperature distribution and future prediction may help to cope with the situation in the near future. Climate forecasting has gone through various faces; in the early days’ people used to predict the behavior qualitatively. Now environmental scientists have developed a quantitative method for forest climate behavior with certain uncertainties. Empirical models have been developed based on regression analysis to estimate temperature distribution. Two models, linear and non linear, use dew point temperature and relative humidity as independent variables. In addition to regression analysis, Artificial Neural Network (ANN) has been utilized to predict the average daily temperatures of Rohri Sindh, a city in Pakistan in the Sindh province. Both empirical models and ANN estimates are in good agreement with the known values of average daily temperatures.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Proceedings of the Pakistan Academy of Sciences: Part A
Proceedings of the Pakistan Academy of Sciences: Part A Computer Science-Computer Science (all)
CiteScore
0.70
自引率
0.00%
发文量
15
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信