Semi-naive Bayesian Forecasts Rainfall on Cloud Platform

Authors

Shengjun Xue, 1. School of Computer and Software, Nanjing University of Information science and Technology, Nanjing 210044, China;;2. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information and Technology, Nanjing 210044, China;
Peiyun Zhang, 1. School of Computer and Software, Nanjing University of Information science and Technology, Nanjing 210044, China;;
Jingyi Chen, 1. School of Computer and Software, Nanjing University of Information science and Technology, Nanjing 210044, China;;

Abstract

Abstract: Rainfall forecast has played an increasingly important role of meteorological services. As cloud platform can improve the efficiency and accuracy of rainfall forecast, it has been applied to forecast rainfall. The recent forecast methods require the independence between all the attributes, but most of the meteorological factors are interdependent, which reduces the accuracy of the prediction. Consequently, a semi-naive Bayesian classification was proposed combined with fuzzy set theory realizing it on cloud platform. At the same time, to improve the accuracy and the efficiency of rainfall forecast, a forecast model was established, which used the historical weather data provided by the weather stations to forecast the next-month rainfall. The experimental results show the method is able to provide higher accuracy and efficiency of rainfall forecast compared with the previous methods.

Recommended Citation

Xue, Shengjun; Zhang, Peiyun; and Chen, Jingyi (2020) "Semi-naive Bayesian Forecasts Rainfall on Cloud Platform," Journal of System Simulation: Vol. 28: Iss. 5, Article 16.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss5/16

First Page

1117

Revised Date

2015-03-13

DOI Link

https://doi.org/

Last Page

1123

CLC

TP311.13

Recommended Citation

Xue Shengjun, Zhang Peiyun, Chen Jingyi. Semi-naive Bayesian Forecasts Rainfall on Cloud Platform[J]. Journal of System Simulation, 2016, 28(5): 1117-1123.