降水预报模型的新方法及验证

IF 0.7 4区 地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES
MAUSAM Pub Date : 2023-10-01 DOI:10.54302/mausam.v74i4.4359
KUMARASWAMY KANDUKURI, BHATRACHARYULU N. CH.
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引用次数: 0

摘要

多年来,许多现实行业的时间序列数据具有不同的预测技术。然而,对于个别预测、自回归、移动平均、自回归移动平均、自回归综合移动平均、人工神经网络、长短期记忆网络、自回归条件异方差/广义自回归条件异方差、组合预测(预测简单平均法、最小方差法、组合回归法)等预测技术,目前还没有形成一致的结论。大多数经验水文时间序列模型不能准确地预报天气。本文重点对现有的不同个体和组合预测与提出的混合随机模型(HSM)预测程序进行了比较研究。为此,我们考虑了印度次大陆的水文时间序列数据来检验所提出的预测模型。作为一个整体,与所有其他传统模型的贡献精度相比,所提出的模型表现良好,并且我们还检查了模型的降维方法,以选择最优数量的预测技术,包括在模型中,以产生最佳预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New method of precipitation forecast model and validation
There is a lot of time series data in many realistic sectors with different forecast techniques over the years. However there is no unanimous conclusion on forecast techniques such as individual forecasts Autoregressive, Moving averages, Autoregressive Moving average, Autoregressive Integrated Moving average, Artificial Neural Network, Long Short Term Memory network and Auto-Regressive Conditional Heteroscedasticity / Generalized Autoregressive Conditional Heteroskedasticity and combination of forecast (simple Average of forecasts, Minimum variance method, and Regression method of the combine). The most empirical hydrological time series models do not accurately forecast the weather. This paper focuses on a comparative study of different existing individual and combination forecasts with the proposed Hybrid Stochastic Model (HSM) forecast procedure. For this we consider a hydrological time series data of the Indian subcontinent to test the proposed forecast model. As a whole in comparison to all other traditional model's contributions accuracy, the proposed model performed well, and also we examined the model's dimension reduction approach to choose an optimum number of forecast techniques to be included in the model to yield the best forecasts.
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来源期刊
MAUSAM
MAUSAM 地学-气象与大气科学
CiteScore
1.20
自引率
0.00%
发文量
1298
审稿时长
6-12 weeks
期刊介绍: MAUSAM (Formerly Indian Journal of Meteorology, Hydrology & Geophysics), established in January 1950, is the quarterly research journal brought out by the India Meteorological Department (IMD). MAUSAM is a medium for publication of original scientific research work. MAUSAM is a premier scientific research journal published in this part of the world in the fields of Meteorology, Hydrology & Geophysics. The four issues appear in January, April, July & October.
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