Simulation of open- and closed-end border irrigation systems using SIRMOD

Khasraghi, Maryam Mahdizadeh; Sefidkouhi, Mohammad Ali Gholami; Valipour, Mohammad

doi:10.1080/03650340.2014.981163

Cited by 152 publications

(13 citation statements)

References 22 publications

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“…By comparing the R 2 of the models, it was determined that time series models are more appropriate for rainfall forecasting in semi‐arid climates. In addition, considering the importance of an accurate estimation of evapotranspiration (Psilovikos and Elhag , Valipour ; Valipour ; Valipour and Eslamian, ), ARIMA can be a powerful model for forecasting evapotranspiration in hydrometeorology and irrigation water requirement (Mahdizadeh Khasraghi et al , ; Valipour, , ; Valipour et al , ). Valipour (, ) forecasted snow water equivalent using the ARIMA model with a relation error of <10%.…”

Section: Sarima and Arima Applications In Hydrological Forecastingmentioning

confidence: 99%

Long-term runoff study using SARIMA and ARIMA models in the United States

Valipour

2015

Met. Apps

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ABSTRACT:In this study, the ability of the seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models was investigated for long-term runoff forecasting in the United States. In the first stage, the amount of runoff is forecasted for 2011 in each US state using the data from 1901 to 2010 (mean of all stations in each state). The results show that the accuracy of the SARIMA model is better than that of the ARIMA model. The relative error of the SARIMA model for all states is <5%. In the second stage, the runoff is forecasted for 2001 to 2011 by using the average annual runoff data from 1901 to 2000. The SARIMA model with periodic term equal to 20, R 2 = 0.91, and mean bias error (MBE) = 1.29 mm is the best model in this stage. According to the obtained results, a trend is observed between annual runoff data in the United States every 20 years or almost a quarter century.KEY WORDS ARIMA; hydrological processes; periodic term; runoff forecasting; seasonal trend Schar et al. (2004) forecasted a seasonal runoff using precipitation from meteorological data assimilation systems. The high correlations suggest that a reliable seasonal runoff forecasting system can be constructed from the statistical relationship between the model-assimilated precipitation and subsequent runoff. Artificial neural network is one of the other approaches for runoff forecasting. In addition, seasonal runoff was simulated using Hydrologic Engineering Center (HEC) models (Anderson et al., 2002). Patry and Marino (1984c) presented a two-stage urban runoff forecast model. This approach was found to be particularly useful for lead times of up to 30 min. Wang et al. (2011) developed a modified rational equation for arid-region runoff estimation successfully. Habib et al. (2007) investigated the effect of local systematic and random errors of the commonly used tipping-bucket (TB) rain gauges on the accuracy of runoff predictions. The computed runoff differences caused by the TB random errors were dependent on the magnitude of the runoff discharge, and on the temporal resolution of the rainfall input. In recent decades, radar information helps researchers in accurate forecasting of runoff data (Rahimi et al., 2014). Marino (1984a, 1984b) investigated the sensitivity and application of different equation models to real-time urban runoff forecasting. Runoff forecasting history SARIMA and ARIMA applications in hydrological forecastingAmong the many methods and models available for investigating long-term forecasts, such as stochastic models, fuzzy theory, artificial neural network (Banihabib et al., 2012;Valipour et al., * Correspondence: M. Valipour, Young Researchers and Elite Club, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. E-mail: vali-pour@hotmail.com 2012a), and rough theory, autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models have been applied based on stochastic theory; considering the serial correl...

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Section: Sarima and Arima Applications In Hydrological Forecastingmentioning

confidence: 99%

Long-term runoff study using SARIMA and ARIMA models in the United States

Valipour

2015

Met. Apps

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238

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“…More accurate estimations within simulation models can enhance the decision-making quality, such as in regard to surface and border irrigation water management Mahdizadeh Khasraghi et al, 2015). The selection of the most appropriate sustainable public transport infrastructure project is usually based on an uncertain and complex procedure since many evaluation criteria are involved in the decision-making process.…”

Section: Introductionmentioning

confidence: 99%

Incorporating carbon footprint with activity-based costing constraints into sustainable public transport infrastructure project decisions

Yang

Lee

Chen

2016

Journal of Cleaner Production

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“…Other useful methods for precipitation analysis are Tropical rainfall measuring mission (TRMM) Multi‐satellite Precipitation Analysis (Pombo et al , ; Dasari and Salgado ), fuzzy technique (Yu et al, ; Hasan et al, ; Kisi and Shiri, ; Kisi and Shiri, ; Rossi et al, ), radar data method (Burlando et al , ; Korsholm et al , ) and spatial and temporal variability analyses (http://www.tandfonline.com/action/doSearch?action=runSearch&type=advanced&searchType=journal&result=true&prevSearch=%2Bauthorsfield%3A%28Wagesho%2C+N%29 et al, ; Foresti and Seed, ; Kim and Lee, ; Wu et al, ). On the other hand, although there are many methods to model hydrological phenomena (Valipour and Eslamian, ; Valipour, , , , , , ; Mahdizadeh Khasraghi et al, ; Rahimi et al, ; Valipour et al, , ), artificial neural network (ANN) is a powerful model for agricultural and forest meteorology analysis. Banihabib et al () and Valipour et al (, , ) forecasted the inflow using ARMA, Auto regressive integrated moving average (ARIMA) and ANN models.…”

Section: Introductionmentioning

confidence: 99%

Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms

Valipour

2015

Meteorological Applications

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284

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ABSTRACT:In this study, annual precipitation was forecast by coding in MATLAB software environment based on a non-linear autoregressive neural network (NARNN), non-linear input-output (NIO) and NARNN with exogenous input (NARNNX). Historical precipitation data (27 precipitation gauge stations located in Gilan, Iran) were used as two 21 year sets from 1968 to 1988 and from 1989 to 2009 for calibration and testing of the networks, respectively. Results showed that the accuracy of the NARNNX was better than that of the NARNN and NIO, based on r values. However, performance of the networks was not satisfactory because the number of neurons in the hidden layer and the roles of training, validation and testing phases were lacking flexibility and change. Optimization of the number of neurons in the hidden layer and the determination of the best role among the different phases led to improvement of network accuracy. The r values were <0.73 only for five stations in the optimized NARNN and <0.74 only for those stations with optimized NIO.

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Simulation of open- and closed-end border irrigation systems using SIRMOD

Cited by 152 publications

References 22 publications

Long-term runoff study using SARIMA and ARIMA models in the United States

Long-term runoff study using SARIMA and ARIMA models in the United States

Incorporating carbon footprint with activity-based costing constraints into sustainable public transport infrastructure project decisions

Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms

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