Application of linear stochastic models to monthly flow data of Kelkit Stream

Yürekli, Kadri; Kurunç, Ahmet; Öztürk, Fazlı

doi:10.1016/j.ecolmodel.2004.08.001

Cited by 67 publications

(35 citation statements)

References 26 publications

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“…An inherent advantage of the SARIMA family of models is that few model parameters are required for describing time series, which exhibit non-stationarity both within and across the seasons. Some useful applications of these models in seasonal river flow forecasting are reported in McKerchar and Dellur (1974), Panu et al (1978), Cline (1981), Govindaswamy (1991) and Yurekli et al (2005). Hydrologists have also widely used stochastic analogy for the analyzing and modeling of hydrologic time series.…”

Section: Background Information On Application Of Stochastic Modelsmentioning

confidence: 98%

Drought forecasting using stochastic models

Mishra

Desai

2005

Stoch Environ Res Ris Assess

406

199

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Drought is a global phenomenon that occurs virtually in all landscapes causing significant damage both in natural environment and in human lives. Due to the random nature of contributing factors, occurrence and severity of droughts can be treated as stochastic in nature. Early indication of possible drought can help to set out drought mitigation strategies and measures in advance. Therefore drought forecasting plays an important role in the planning and management of water resource systems. In this study, linear stochastic models known as ARIMA and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to forecast droughts based on the procedure of model development. The models were applied to forecast droughts using standardized precipitation index (SPI) series in the Kansabati river basin in India, which lies in the Purulia district of West Bengal state in eastern India. The predicted results using the best models were compared with the observed data. The predicted results show reasonably good agreement with the actual data, 1-2 months ahead. The predicted value decreases with increase in lead-time. So the models can be used to forecast droughts up to 2 months of lead-time with reasonably accuracy.

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Section: Background Information On Application Of Stochastic Modelsmentioning

confidence: 98%

Drought forecasting using stochastic models

Mishra

Desai

2005

Stoch Environ Res Ris Assess

406

199

View full text Add to dashboard Cite

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“…Для оценивания эффективности прогнозы обычно сравниваются с прогнозами, получа-емыми моделями ARIMA [5][6][7].…”

Section: анализ литературных данных и постановка проблемыunclassified

Hybrid mathematical models and methods of time series forecasting taking into account external factors

Щелкалин¹

2014

EEJET

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“…Forecasting monthly rainfall data using various ARIMA models was done by [7], whereas [8] carried out streamflow prediction on a medium sized basin in Mississippi. The ARIMA model was applied to monthly data from Kelkit Stream watershed by [9]. [10] reviewed the performance of two stochastic models (ThomasFiering and ARIMA) on Yesilirmak River, Turkey.…”

Section: Arima Modelmentioning

confidence: 99%

Flood Analysis in Langat River Basin Using Stochatic Model

Huang¹

2016

Geomate

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This study analyzed the annual maximum stage readings of three rivers in Langat River Basin for flood forecasting using Autoregressive Integrated Moving-average(ARIMA) model. Model identification was done by visual inspection on the Autocorrelation Function(ACF) and Partial Autocorrelation Function(PACF). The model parameters were computed using the Maximum Likelihood (ML) method. In model verification, the chosen criterion for model parsimony was the Akaike Information Criteria Corrected(AICC) and the diagnostic checks include residuals' independence, homoscedasticity and normal distribution. The best ARIMA models for the Dengkil, Kg. Lui and Kg. Rinching series were (1,1,0), (1,1,0) and (1,1,1) respectively, with their AICC values of 133.736, 55.348 and 42.292. Homoscedasticity was confirmed with the Breusch-Pagan test giving pvalues of 0.145, 0.195 and 0.747 for the Dengkil, Kg. Lui and Kg. Rinching models respectively. Forecast series up to a lead time of eight years were generated using the accepted ARIMA models. Model accuracy was checked by comparing the synthetic series with the original series. Results show that the ARIMA models for the rivers and the forecast series were adequate. In conclusion, the Box-Jenkins approach to ARIMA modelling was found to be appropriate and adequate for the rivers. The flood forecast up to a lead time of eight years for the three models exhibit a straight line with near constant streamflow values showing that the forecast values were similar to the last recorded observation.

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Application of linear stochastic models to monthly flow data of Kelkit Stream

Cited by 67 publications

References 26 publications

Drought forecasting using stochastic models

Drought forecasting using stochastic models

Hybrid mathematical models and methods of time series forecasting taking into account external factors

Flood Analysis in Langat River Basin Using Stochatic Model

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