The river runoff forecast based on the modeling of time series

被引：5
|
作者：
Nigam, R. [1 ]
Nigam, S. [2 ]
Mittal, S. K. [3 ]
机构：
[1] Rajeev Gandhi Tech Univ, Bhopal, MP, India
[2] LNCTS, Bhopal, MP, India
[3] MANIT, Bhopal, MP, India
来源：
RUSSIAN METEOROLOGY AND HYDROLOGY | 2014年 / 39卷 / 11期
关键词：
River Runoff; RUSSIAN Meteorology; Radial Basis Function Neural Network; ARMA Model; ARIMA Model;
D O I：
10.3103/S1068373914110053
中图分类号：
P4 [大气科学（气象学）];
学科分类号：
0706 ; 070601 ;
摘要：
Discussed are the methods of stochastic modeling the precipitation runoff time series and fields. Discussed are the structural attributes, scope, boundary conditions and various improvements of the univariate Autoregressive Integrated Moving Average (ARIMA) and the multivariate Transfer Function Model (TFM). Presented are the comparative studies of existing models of the neural network. An attempt is made to investigate various geographical locations and various applications of the river runoff forecast.
引用
收藏
页码：750 / 761
页数：12
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