BESTED: An Exponentially Smoothed Spatial Bayesian Analysis Model for Spatio-temporal Prediction of Daily Precipitation

被引：3

作者：

Das, Monidipa ^{[1
]}

Ghosh, Soumya K. ^{[1
]}

机构：

[1] Indian Inst Technol Kharagpur, Dept Comp Sci & Engn, Kharagpur, W Bengal, India

来源：

25TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2017) | 2017年

关键词：

Spatial Bayesian network; Spatio-temporal prediction; Time series; Exponential smoothing; Precipitation; NETWORK;

D O I：

10.1145/3139958.3140040

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a novel data-driven model (BESTED), based on spatial Bayesian network with incorporated exponential smoothing mechanism, for predicting precipitation time series on daily basis. In BESTED, the spatial Bayesian network helps to efficiently model the influence of spatially distributed variables. Moreover, the incorporated exponential smoothing mechanism aids in tuning the network inferred values to compensate for the unknown factors, influencing the precipitation rate. Empirical study has been carried out to predict the daily precipitation in West Bengal, India, for the year 2015. The experimental result demonstrates the superiority of the proposed BESTED model, compared to the other benchmarks and state-of-the-art techniques.

引用

页数：4

共 5 条

[1]

Cressie N., 2015, Statistics for spatiotemporal data

[2]

Das Monidipa, 2014, 2014 9th International Conference on Industrial and Information Systems (ICIIS), P1, DOI 10.1109/ICIINFS.2014.7036528

[3] semBnet: A semantic Bayesian network for multivariate prediction of meteorological time series data [J].