RETRACTED: Weather forecast prediction and analysis using sprint algorithm (Retracted Article)

被引:1
作者
Krishnaveni, N. [1 ]
Padma, A. [2 ]
机构
[1] PSR Engn Coll, Dept Comp Sci & Engn, Sivakasi, India
[2] Kongunadu Coll Engn & Technol, Dept Informat Technol, Namakkal, India
关键词
Weather forecasting; Data mining; WEKA tool; SPRINT algorithm; Climate data; DATA ANALYTICS;
D O I
10.1007/s12652-020-01928-w
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Weather forecasting is an emerging domain that predicts the weather condition at a particular location at a particular time. Weather forecasting is considered as the most sensitive research field which facing a lot of real-time issues such as inaccurate prediction, lack of handling in huge data volume and inadequate in technology advancement. In this paper, we propose the SPRINT algorithm which is works with the principle of the decision tree. The experimental work is carried out with climate dataset and applied on WEKA tool. Based on the climate parameters such as Outlook, Temperature, Humidity, and Windy the data is classified into sunny, overcast and rainy. From the obtained result the weather is predicted, to prove the proposed methods of proficiency in accuracy level. Performance comparison is done with the existing method navie Bayes, both results are plotted on the graph. The outcome proves SPRINT algorithm is efficient and accurate in predicting the weather conditions.
引用
收藏
页码:4901 / 4909
页数:9
相关论文
共 14 条
  • [1] Almgren K, 2019, WEATHER DATA ANAL US
  • [2] Alshareef A, 2015, 2015 SCIENCE AND INFORMATION CONFERENCE (SAI), P572, DOI 10.1109/SAI.2015.7237200
  • [3] Biswas M., 2018, Int. J. Comput. Appl, V182, P20, DOI [DOI 10.5120/IJCA2018918265, 10.5120/ijca2018918265]
  • [4] Temperature prediction using fuzzy time series
    Chen, SM
    Hwang, JR
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2000, 30 (02): : 263 - 275
  • [5] An innovative framework for supporting big atmospheric data analytics via clustering-based spatio-temporal analysis
    Cuzzocrea, Alfredo
    Gaber, Mohamed Medhat
    Fadda, Edoardo
    Grasso, Giorgio Mario
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2019, 10 (09) : 3383 - 3398
  • [6] Kothapalli S, 2017, 2017 IEEE INTERNATIONAL CONFERENCE ON POWER, CONTROL, SIGNALS AND INSTRUMENTATION ENGINEERING (ICPCSI), P1567, DOI 10.1109/ICPCSI.2017.8391974
  • [7] Mahmood MR, 2019, INNOVATIONS ELECTRON
  • [8] Spatial cumulative sum algorithm with big data analytics for climate change detection
    Manogaran, Gunasekaran
    Lopez, Daphne
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2018, 65 : 207 - 221
  • [9] An ensemble of neural networks for weather forecasting
    Maqsood, I
    Khan, MR
    Abraham, A
    [J]. NEURAL COMPUTING & APPLICATIONS, 2004, 13 (02) : 112 - 122
  • [10] Salman MG, 2015, INT C ADV COMP SCI I, P281, DOI 10.1109/ICACSIS.2015.7415154