Survey on Weather Prediction using Big Data Analystics

被引:0
作者
Reddy, P. Chandrashaker [1 ]
Babu, A. Suresh [2 ]
机构
[1] St Martins Engn Coll, Comp Sci & Engn, Hyderabad, Telangana, India
[2] JNTUA Coll Engn, Comp Sci & Engn, Ananthapuramu, AP, India
来源
PROCEEDINGS OF THE 2017 IEEE SECOND INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND COMMUNICATION TECHNOLOGIES (ICECCT) | 2017年
关键词
Prediction; Rainfall; Big data; Regression; Weather;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Huge losses occur all most on a daily basis due to many natural calamities like earth quakes, storms, cyclones etc. that will have adverse effects on the lives of billions of people. In the modern world, the prediction of environmental impact had become a challenging task. A rigorous rainfall prediction system is very useful and important for most of the country like India, because these are mainly depends on agriculture. For investigating the yield productivity, use of water resources and rainfall, prediction is very important. Many Statistical and Predictive models for rainfall forecasting system are available in the literature. This models provides less accuracy prediction models in large-scale rainfall forecasting (Yearly basis) due to the dynamic temperament of climate conditions. Weather damage prediction is an important research issue where it relates, both science and technology in order to forecast the weather damage of a particular location. We have a many good prediction system but there is no proper estimation for future damage because the time span between the present moment and time for which forecasting is being made and released is varied. By collecting previous datasets from datacenters like India Meteorological Departments in a specific location and using regression analysis, we can predict (numerical data) the values of damage. In this paper we mainly discuss about the various weather predictions models proposed by different researchers.
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页数:6
相关论文
共 27 条
[1]   Application of artificial neural networks to rainfall forecasting in Queensland, Australia [J].
Abbot, John ;
Marohasy, Jennifer .
ADVANCES IN ATMOSPHERIC SCIENCES, 2012, 29 (04) :717-730
[2]  
Abhishek K., 2012, Proceedings of the 2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012), P82, DOI 10.1109/ICSGRC.2012.6287140
[3]  
[Anonymous], 2006, J IND GEOPHYS UNION
[4]  
[Anonymous], 2005, PHIL T R SOC B
[5]  
Bendre MR, 2015, 2015 1ST INTERNATIONAL CONFERENCE ON NEXT GENERATION COMPUTING TECHNOLOGIES (NGCT), P744, DOI 10.1109/NGCT.2015.7375220
[6]  
Dabhi Vipul K., 2014, Advances in Artificial Intelligence, DOI 10.1155/2014/717803
[7]  
Darema Frederica, 2015, 2015 IEEE 22 INT C H
[8]  
Darji Mohini P., RAINFALL FORECASTING
[9]  
Dennis A., 2013, 2013 NT C CYB EN DIS
[10]  
Dennis A., 2013, 2013 2 IIAI INT C AD