A combined transfer-function noise model to predict the dynamic behavior of a full-scale primary sedimentation tank

El‐Din, Ahmed G.; Smith, Daniel

doi:10.1016/s0043-1354(02)00089-1

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…If the ARIMA model is correct, the estimated autocorrelations of the residuals are uncorrelated and distributed approximately normally about zero. Therefore, the correlograms of the residuals are examined for correlations greater then two standard deviations since large correlations may have indicated model inadequacies, especially if they are at lower lags (El-Din and Smith 2002). If some of the RACFs are significantly differing from zero, this explains that the model is not adequate.…”

Section: Diagnostic Checkmentioning

confidence: 99%

Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey

Durdu

2010

Stoch Environ Res Risk Assess

105

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In the present study, a seasonal and non-seasonal prediction of the Standardized Precipitation Index (SPI) time series is addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SAR-IMA) to predict drought in the Büyük Menderes river basin using SPI as drought index. Temporal characteristics of droughts based on SPI as an indicator of drought severity indicate that the basin is affected by severe and more or less prolonged periods of drought from 1975 to 2006. Therefore, drought prediction plays an important role for water resources management. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, diagnostic checking. In model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of the SPI series, different ARIMA models are identified. The model gives the minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) is selected as the best fit model. Parameter estimation step indicates that the estimated model parameters are significantly different from zero. Diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicated that the residuals are independent, normally distributed and homoscedastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The ARIMA models developed to predict drought found to give acceptable results up to 2 months ahead. The stochastic models developed for the Büyük Menderes river basin can be employed to predict droughts up to 2 months of lead time with reasonably accuracy.

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Section: Diagnostic Checkmentioning

confidence: 99%

Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey

Durdu

2010

Stoch Environ Res Risk Assess

105

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“…Broadly, the S td can be chosen as the superior method of the second station as well. Figures 6-8 examine the existence of a significant periodicity in model residuals and modeling accuracy [41,42]. Figure 6 shows the cumulative periodicity of the residuals of the ARIMA models.…”

Section: Modeling Resultsmentioning

confidence: 99%

A Methodology for Forecasting Dissolved Oxygen in Urban Streams

Stajkowski

Zeynoddin

Farghaly

et al. 2020

Water

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Real-time monitoring of river water quality is at the forefront of a proactive urban water management strategy to meet the global challenge of vital freshwater resource sustainability. The concentration of dissolved oxygen (DO) is a primary indicator of the health state of the aquatic habitats, and its modeling is crucial for river water quality management. This paper investigates the importance of the choices of different techniques for preprocessing and stochastic modeling for developing a simple and reliable linear stochastic model for forecasting DO in urban rivers. We describe several methods of evaluation, preprocessing, and modeling for the DO parameter time series in the Credit River, Ontario, Canada, to achieve the optimum data preprocessing and input selection techniques and consequently obtain the optimum performance of the stochastic models as an effective river management tool. The Manly normalization and standardization (Std) methods were chosen for preprocessing the time series. Modeling the preprocessed time series using the stochastic autoregressive integrated moving average (ARIMA) model resulted in very accurate forecasts with a negligible difference from sole normalization and spectral analysis (Sf) methods.

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“…In order to test the null hypothesis (that a current set of autocorrelations is white noise), test statistics were calculated for different total numbers of successive lagged autocorrelations using the Box-Pierce statistics (Q(r) test)) to test the adequacy of the model. Q(r) values were compared to a critical test value of  2 distribution with respect to their degrees of freedom at the probability level of 5% (El-Din and Smith, 2002).…”

Section: Methodsmentioning

confidence: 99%

Time series modelling of increased soil temperature anomalies during long period

Shirvani¹,

Moradi²,

Moosavi³

2015

International Agrophysics

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Soil temperature just beneath the soil surface is highly dynamic and has a direct impact on plant seed germination and is probably the most distinct and recognisable factor governing emergence. Autoregressive integrated moving average as a stochastic model was developed to predict the weekly soil temperature anomalies at 10 cm depth, one of the most important soil parameters. The weekly soil temperature anomalies for the periods of January1986-December 2011 and January 2012-December 2013 were taken into consideration to construct and test autoregressive integrated moving average models. The proposed model autoregressive integrated moving average (2,1,1) had a minimum value of Akaike information criterion and its estimated coefficients were different from zero at 5% significance level. The prediction of the weekly soil temperature anomalies during the test period using this proposed model indicated a high correlation coefficient between the observed and predicted data - that was 0.99 for lead time 1 week. Linear trend analysis indicated that the soil temperature anomalies warmed up significantly by 1.8°C during the period of 1986-2011.

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A combined transfer-function noise model to predict the dynamic behavior of a full-scale primary sedimentation tank

Cited by 17 publications

References 10 publications

Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey

Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey

A Methodology for Forecasting Dissolved Oxygen in Urban Streams

Time series modelling of increased soil temperature anomalies during long period

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