نتایج جستجو برای: monthly flow prediction

تعداد نتایج: 754787  

2007
M. J. Diamantopoulou V. Z. Antonopoulos D. M. Papamichail

Axios River is one of the most important transboundary rivers between the Greek and the neighbour country FYROM in the Balkan area. In this paper, Artificial Neural Networks (ANNs) were used to derive and to develop models for prediction the monthly values of some water quality parameters of the river Axios at a station located at Axioupolis site of Greece near the Greece FYROM borders by using...

Journal: :journal of water sciences research 2012
a.r mardookhpour

in order to determine hydrological behavior and water management of sepidroud river (north of iran-guilan) the present study has focused on stream flow prediction by using artificial neural network. ten years observed inflow data (2000-2009) of sepidroud river were selected; then these data have been forecasted by using neural network. finally, predicted results are compared to the observed dat...

Journal: :تحقیقات جغرافیایی 0
حمیدرضا عزیزی گروه جغرافیا دانشگاه آزاد اسلامی واحد نجف آباد مجید منتظری دانشگاه اصفهان

forecasting of temperature is a very important in meteorology. air temperature prediction is of a concern in environment, industry and agriculture. temperature with precipitation are important factors in meteorology and are used in classification of climate. in this paper we want to predict average monthly temperature for chosen station of isfahan province. an artificial neural network is a pow...

Ahmad Fakheri Fard Farshad Ahmadi, Keivan KHalili Yagub Dinpashoh

One of the most important hydrological time series task is to determine if there is any trend in the data and how to achieve stationarity when there is nonstationarity behavior in data. Detecting trend and stationarity in hydrological time series may help us to understand the possible links between hydrological processes and global climate changes. In this study yearly, monthly and daily stream...

Ahmad Fakheri Fard Farshad Ahmadi, Keivan KHalili Yagub Dinpashoh

One of the most important hydrological time series task is to determine if there is any trend in the data and how to achieve stationarity when there is nonstationarity behavior in data. Detecting trend and stationarity in hydrological time series may help us to understand the possible links between hydrological processes and global climate changes. In this study yearly, monthly and daily stream...

Considering the fact that natural gas is a widely used energy source,  the prediction of its consumption can be useful (Derek LAM, 2013). As Iran has one of the largest gas reserves in the world, its consumption in the country can affect the worldwide price of gas, Therefore, the current research is useful both from economic and environmental point of view. ...

2006
JIAN-YI LIN CHUN-TIAN CHENG KWOK-WING CHAU

Abstract Accurate timeand site-specific forecasts of streamflow and reservoir inflow are important in effective hydropower reservoir management and scheduling. Traditionally, autoregressive movingaverage (ARMA) models have been used in modelling water resource time series as a standard representation of stochastic time series. Recently, artificial neural network (ANN) approaches have been prove...

2000
D. Nagesh Kumar Upmanu Lall Michael R. Petersen

Streamflow disaggregation is used to preserve statistical attributes of time series across multiple sites and timescales. Several algorithms for spatial disaggregation and for disaggregation of annual to monthly flows are available. However, the disaggregation of monthly to daily or weekly to daily flows remains a challenge. A new algorithm is presented for simultaneously disaggregating monthly...

In this research, monthly rainfall of Shiraz synoptic station from March 1971 to February 2016 was studied using different time series models by ITSM Software. Results showed that the ARMA (1,12) model based on Hannan-Rissanen method was the best model which fitted to the data. Then, to assess the verification and accuracy of the model, the monthly rainfall for 60 months (from March 2011 to Feb...

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