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

Tadesse, Kassahun Birhanu; Dinka, Megersa Olumana

doi:10.1515/jwld-2017-0088

Cited by 31 publications

(20 citation statements)

References 16 publications

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“…1. It shows that the data remains stationary but it should be further tested by Augmented Dickey-Fuller test, KPSS, PP test and Mann Kendall's trend test (Tadesse et al, 2017). Dam inflow series is usually a random factor, where the maximum inflows are recorded in the rainy season and minimum in the summer season.…”

Section: Resultsmentioning

confidence: 99%

“…This SARIMA model is capable of forecasting monthly reservoir inflow, even for low values and short-term forecasting than the hybrid Artificial Neural Network-Genetic Algorithm model (Moeeni et al, 2017). Also Tadesse et al (2017) employed the SARIMA model to forecast the inflows of Waterval River. Based on this past research information, the present study aims to forecast the inflow rate of the Palar-Porundalar dam in the Dindigul district of Tamil Nadu using seasonal ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the monthly inflow rate of the Palar-Porundalar dam in Tamil Nadu using SARIMA model

Chitra

Selvi

Mahendran

2019

JANS

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Dam inflow forecasting information is essential for planning and management of the dam system. Time series analysis is the most commonly employed technique to forecast the future values based on historical information. In this study, Palar-Porandalar dam in Tamil Nadu inflow series were forecasted in R software package using ARIMA model with seasonal factors. The monthly inflow series of the dam from 2003 January to 2017 December were used as an input source for modeling and forecasting process. Mann-Kendall’s trend test and various Stationarity test were performed to verify the Stationary nature of the data set. From the Correlogram plot, different models were identified; their parameters were optimized and residuals were diagnostically tested using Autocorrelation plot and Ljung Box test. Finally, the best model was selected based on minimum Akaike Information Criteria (AIC), BIC, RMSE and Theil’s U statistic values. From various models, SARIMA (0, 0, 1) (1, 0, 2)12 model was selected as the best one for forecasting the inflow series.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting the monthly inflow rate of the Palar-Porundalar dam in Tamil Nadu using SARIMA model

Chitra

Selvi

Mahendran

2019

JANS

View full text Add to dashboard Cite

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“…For SARIMA foresight predicting we use internal financial, social and ecological companies' factors and external (list of components is in Appendix A). For this purpose, the SARIMA model was used by the authors (Tadesse and Dinka, 2017) (Chikobvu and Sigauke, 2012) and also an annual linear trend was added. Each component was forecasted in a confidence interval with a 10% error probability (alpha =10%).…”

Section: Foresight Forecast 2030mentioning

confidence: 99%

China and Russia Energy Strategy Development: Arctic LNG

Steblyanskaya

Qingchao²,

Razmanova

et al. 2021

IJEEP

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Nowadays, the LNG market is a derivative of the traditional gas market and has certain advantages over pipeline gas supplies. Many countries, including the Russian Federation, are trying to consolidate their positions in the relatively new and growing LNG market. In the paper, Sino-Russia Energy strategy perspectives until 2030 are being analyzed in detail. The authors analyze the Arctic LNG case as the most crucial for both countries' collaboration. The Arctic is considered as the new strategic frontier of China. China is a critical Arctic stakeholder as it is written in the newly released white paper China's Arctic Policy. The authors use Python 3.4. modeling for testing the influence of economic, social and environmental factors on Sino-Russia energy collaboration. The methodology consists of foresight analysis, including principal component isolation (further-PCA) method and SARIMA analysis. Research results show that the values of the components in Russia and China industries are drastically different. However, some components would be significantly developed due to Russia's existing trends by 2030. Indeed, it can be concluded that the dissimilarity between Russia and China' oil and gas industries would increase by 2030, as indicated by the first, second and fourth components. China's oil and gas industry has a stable trend for development.

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“…The time series plot showed that the data exhibited stationary and the quality of stationarity of the observation was further tested by Augmented Dickey-Fuller test (ADF), KPSS test, PP test (Table 2). The probability value of ADF and PP test were less than 0.05 and greater than 0.05 for KPSS test for the rainfall data [15]. Thus, the data set was considered to be stationary at 5 and 6 lag.…”

Section: Stationaritymentioning

confidence: 99%

SARIMA Modelling and Forecasting of Monthly Rainfall Patterns for Coimbatore, Tamil Nadu, India

Kokilavani

Pangayarselvi

Ramanathan

et al. 2020

CJAST

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Weather forecasting is an important subject in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. The present paper describes an empirical study for modeling and forecasting the time series of monthly rainfall patterns for Coimbatore, Tamil Nadu. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The best SARIMA models were selected based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) and the minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The study has shown that the SARIMA (0,0,0)(2,0,0)12 model was appropriate for analysing and forecasting the future rainfall patterns. The Root Means Square Error (RMSE) values were found to be 52.37 and proved that the above model was the best model for further forecasting the rainfall.

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

Cited by 31 publications

References 16 publications

Forecasting the monthly inflow rate of the Palar-Porundalar dam in Tamil Nadu using SARIMA model

Forecasting the monthly inflow rate of the Palar-Porundalar dam in Tamil Nadu using SARIMA model

China and Russia Energy Strategy Development: Arctic LNG

SARIMA Modelling and Forecasting of Monthly Rainfall Patterns for Coimbatore, Tamil Nadu, India

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