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Identification of appropriate stochastic model for forecasting monsoonal river inflows

机译:确定用于预测季风河流量的适当随机模型

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There are various mathematical models for generating river inflows, such as the autorcgressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (AIM) models. However, use of these models has been limited to perennial river modeling with high and low flows. Rivers in Rajasthan in India are mostly monsoonal, having less than 5 per cent of annual flow in the dry season. Of the limitless combinations of these models, the present study was undertaken to develop the most appropriate stochastic model for forecasting inflows of a monsoonal river. The Jakham river is a monsoonal river in southern Rajasthan. Available monthly inflow data for 36 years were used for stochastic model building for themonsoonal river which can be used for forecasting inflows. A systematic approach, considering autocorrelation function (ACF), partial autocorrelation function (PACF), inverse autocorrelation function (IACF) and inverse partial autocorrelation function (IPACF) analysis, was followed for identification of tentative stochastic models. With the help of estimation of relevant parameters and inflow forecasting, an appropriate model was identified for forecasting monsoonal inflows to the Jakham river.
机译:有多种用于生成河流流入的数学模型,例如自回归(AR),移动平均(MA),自回归移动平均(ARMA)和自回归综合移动平均(AIM)模型。但是,这些模型的使用仅限于高流量和低流量的多年生河流模型。印度拉贾斯坦邦的河流多为季风性河流,在干旱季节的年流量不到5%。在这些模型的无限组合中,进行了本研究,以开发最合适的随机模型来预测季风河的流入量。 Jakham河是拉贾斯坦邦南部的季风河。使用了36年可用的每月流入量数据来建立季风河的随机模型,该模型可用于预测流入量。一种系统的方法,考虑了自相关函数(ACF),部分自相关函数(PACF),逆自相关函数(IACF)和逆自相关函数(IPACF)分析,用于确定暂定随机模型。借助相关参数的估计和流量的预测,确定了一个合适的模型来预测贾康河的季风流量。

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