Application of Software Packages for Monthly Stream Flow Forecasting of Kangsabati River in India

Singh, Mehakpreet; Singh, R.D.; Shinde, V. T.

doi:10.5120/2416-3231

Cited by 12 publications

(13 citation statements)

References 8 publications

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“…The first step for model identification is the examination of the river flows stationarity. For this purpose, graphical methods: sample autocorrelation function (ACF) and sample partial autocorrelation functions (PACF) [SINGH et al 2012] were used. Moreover, unit root test has been made using Augmented Dickey-Fuller (ADF) [DICK- EY, FULLER 1979], Phillips-Perron (PP) [PHILLIPS, PERRON 1988], and Kwiatkowski-Philips-SchmidtShin (KPSS) [KWIATKOWSKI et al 1992] at 0.5 significance level (α = 0.05).…”

Section: Sarima Modellingmentioning

confidence: 99%

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Tadesse

Dinka

2017

Journal of Water and Land Development

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Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann-Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI) and Hannan-Quinn (HQ) criteria, SARIMA (3, 0, 2) x (3, 1, 3) 12 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

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Section: Sarima Modellingmentioning

confidence: 99%

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Tadesse

Dinka

2017

Journal of Water and Land Development

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“…Solis et al (2008) used ARIMA model for forecasting streamflow of a Mexican river (Solis et al, 2008). Singh et al (2011) forecasted the monthly streamflow of Kangsabati River in India by applying ARIMA and X-12-ARIMA (Singh et al, 2011). Ruqaya (2011) used ARIMA model for forecasting the inflow into Dokan reservoir in Iraq (AlMasudi, 2011).…”

Section: Introductionmentioning

confidence: 99%

Daily Streamflow Prediction for Khazir River Basin Using ARIMA and ANN Models

Kassem

Raheem²,

Khidir

2020

ZJPAS

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The present study used both Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models for Khazir river basin to simulate the daily flow at Asmawa and Khanis gauge stations. Asmawa station lies on Khazir River while Khanis lies on Gomel River as a tributary of Khazir River. In the stochastic ARIMA model, the Autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to determine how robust the ARIMA model is in predicting the streamflow. In this study, the Akaike Information Criterion (AIC) formula and Bayesian information criterion (BIC) were used to evaluate which model is more accurate. The results of this study showed that models of order ARIMA are (2,0,0)(2,1,0) and (2,0,1)(2,1,0) were found much better than the other models for generating and forecasting daily flow time series for aforementioned stations. Coefficients of determination (R 2) were found 0.77 and 0.85 for both Asmawa and Khanis stations, respectively. However, two types of ANN models were used for analyzing the daily flow records of the same two aforementioned stations, Multilayer Perceptron (MLP) and Radial Basis Function (RBF). ANN-MLP model was found to be more accurate than the ANN-RBF for generating and forecasting the daily flow time series as the coefficient of determination provided by ANN-MLP for both stations were 0.83 and 0.85, respectively. In addition, the coefficients of determination produced by the ANN-RBF for both stations were 0.66 and 0.55, respectively. Based on the values of (R 2) and (RMSE) obtained in the current work, one can conclude that the ANN-MLP model is the most accurate model among the others in terms of predicting the streamflow for Asmawa station, whereas the performance of both ARIMA and ANN-MLP models for the Khanis station is the same.

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“…As the first step of the prediction, the data are analyzed to find the stationarity, to determine the seasonal effect of the data. To identify the stationarity, the autocorrelation function (ACF) and partial autocorrelation functions (PACF) [24] are used. The requirement of fitting an ARIMA model depends on the series to be stationary.…”

Section: Model Identificationmentioning

confidence: 99%

A Study of Seasonal ARIMA Model-Based Forecasting Method for Intelligent Food Control in a Livestock Environment

Sivamani

Venkatesan²

2018

IJIoTA

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Most of the high and medium quality hays are imported from different countries for the livestock feedlots. As a fact, increasing production cost is becoming one of the primary problems in the livestock production. To minimize the cost spent on the hay import, the forecasting has to be precise. More than the previous year food stock data; the accumulated feed intake of Beef cattle can give an accurate forecast. Therefore, in this paper, Seasonal-Autoregressive Integrated Moving Average (SARIMA) model is used to forecast the food stock requirement in the livestock barn over a simulated data. By identifying the model implementation, The best fit model is identified using the SARIMA model, and the predicted values are compared with the actual data, to provide an accurate forecasting of the food supply. .

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Application of Software Packages for Monthly Stream Flow Forecasting of Kangsabati River in India

Cited by 12 publications

References 8 publications

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

Daily Streamflow Prediction for Khazir River Basin Using ARIMA and ANN Models

A Study of Seasonal ARIMA Model-Based Forecasting Method for Intelligent Food Control in a Livestock Environment

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