A neural network model for short term river flow prediction

Teschl, Reinhard; Randeu, Walter

doi:10.5194/nhess-6-629-2006

Cited by 28 publications

(24 citation statements)

References 10 publications

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“…These results were consistent with the findings of previous studies (Teschl and Randeu, 2006;Li et al, 2012;Wu and Chau, 2010), which showed the ability of the ANN model to determine an atmospheric link between SST anomalies and precipitation over the inland area of the Persian Gulf.…”

Section: Discussionsupporting

confidence: 92%

“…Since large differences existed in the means and variations between the parameters, the data were normalized (Trenberth, 1994;Teschl and Randeu, 2006;Guérémy, 2012) before they were used in the model. After normalization, all time series of monthly rainfall and climate indices were in the range of 0 and 1 (see Appendix: Eq.…”

Section: Description Of Methodsmentioning

confidence: 99%

“…Long-term prediction fills the gap between short-range weather forecasting and climate prediction, and points to timescales of more than 1 month to 1 year (Tourigny and Jones, 2009). The main source of the predictability results from large-scale atmospheric circulation anomalies due to tropical sea surface temperature (SST) anomalies (Teschl and Randeu, 2006). Sea surface temperature anomalies have relatively large timescales, and may be predictable at a useful level of skill up to a season or a year (Shukla et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

Sigaroodi

Chen

Ebrahimi

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Long-term precipitation forecasts can help to reduce drought risk through proper management of water resources. This study took the saline Maharloo Lake, which is located in the north of Persian Gulf, southern Iran, and is continuously suffering from drought disaster, as a case to investigate the relationships between climatic indices and precipitation. Cross-correlation in combination with stepwise regression technique was used to determine the best variables among 40 indices and identify the proper time lag between dependent and independent variables for each month. The monthly precipitation was predicted using an artificial neural network (ANN) and multi-regression stepwise methods, and results were compared with observed rainfall data. Initial findings indicated that climate indices such as NAO (North Atlantic Oscillation), PNA (Pacific North America) and El Niño are the main indices to forecast drought in the study area. According to R 2 , root mean square error (RMSE) and Nash-Sutcliffe efficiency, the ANN model performed better than the multi-regression model, which was also confirmed by classification results. Moreover, the model accuracy to forecast the rare rainfall events in dry months (June to October) was higher than the other months.From the findings it can be concluded that there is a relationship between monthly precipitation anomalies and climatic indices in the previous 10 months in Maharloo Basin. The highest and lowest accuracy of the ANN model were in September and March, respectively. However, these results are subject to some uncertainty due to a coarse data set and high system complexity. Therefore, more research is necessary to further elucidate the relationship between climatic indices and precipitation for drought relief. In this regard, consideration of other climatic and physiographic factors (e.g., wind and physiography) can be helpful.

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Section: Discussionsupporting

confidence: 92%

Section: Description Of Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

Sigaroodi

Chen

Ebrahimi

et al. 2014

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…A MLP is a so-called 'feedforward' neural network because all the information flows in one direction. The neurons of one layer are connected with the neurons of the following layer without feedback (Teschl and Randeu, 2006). The weights adjustment was performed by a back propagation algorithm: weights are modified to reduce the error occurrence between actual and desired network outputs backward from the output layer to the input layer (Backhaus et al, 2003).…”

Section: Methodsmentioning

confidence: 99%

Predicting summer rainfall in the Yangtze River basin with neural networks

Hartmann

Becker

King

2007

Intl Journal of Climatology

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Summer rainfall in the Yangtze River basin is predicted using neural network techniques. Input variables (predictors) for the neural network are the Southern Oscillation Index (SOI), the East Atlantic/Western Russia (EA/WR) pattern, the Scandinavia (SCA) pattern, the Polar/Eurasia (POL) pattern and several indices calculated from sea surface temperatures (SST), sea level pressures (SLP) and snow data from December to April for the period from 1993 to 2002. The output variable of the neural network is rainfall from May to September for the period from 1994 to 2002, which was previously classified into six different regions by means of a principal component analysis (PCA). Rainfall is predicted from May to September 2002.The winter SST and SLP indices are identified to be the most important predictors of summer rainfall in the Yangtze River basin. The Tibetan Plateau snow depth, the SOI and the other teleconnection indices seem to be of minor importance for an accurate prediction. This may be the result of the length of the available time series, which does not allow a deeper analysis of the impact of multi‐annual oscillations.The neural network algorithms proved to be capable of explaining most of the rainfall variability in the Yangtze River basin. For five out of six regions, our predictions explain at least 77% of the total variance of the measured rainfall. Copyright © 2007 Royal Meteorological Society

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“…In the context of this study a multi-layer perceptron NN design is applied, which is known as a "feed-forward" NN with all information flowing in one direction. The neurons of one layer are connected with the neurons of the following layer without feedback (Teschl and Randeu, 2006). The signals flowing on the connections are scaled by adjustable parameters, which are called weights (Principe et al, 2000).…”

Section: Methodsmentioning

confidence: 99%

Neural Network Based Seasonal Predictions of Lake-Effect Snowfall

Hartmann

2012

Atmosphere-Ocean

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In the case of the city of Buffalo (New York, United States), located on the eastern shore of Lake Erie and, therefore, strongly influenced by the lake-effect, total monthly snowfall was predicted one to six months in advance. For this, neural network (NN) techniques, specifically a multi-layer perceptron, as well as a multiple linear regression (LR) model were applied. The period of analysis comprised 28 years from January 1982 to December 2009. Input data included surface air temperature; the temperature difference between the lake surface water temperature (LSWT) and the 850 hPa air temperature; the u-component of the wind (u-wind) and the v-component of the wind (v-wind), geopotential height (GPH) over Lake Erie and the surrounding regions at the 1000, 925, 850 and 700 hPa levels as well as the surface pressure; the 500 hPa GPH over James Bay, Canada; the surface pressure over the Great Plains; and the mean water temperature and LSWT of Lake Erie, as well as the amount of ice cover. Moreover, several teleconnection indices were implemented: the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the Pacific/North American (PNA) pattern, the Southern Oscillation Index (SOI) and the Pacific Decadal Oscillation (PDO).Different lead times for the input variables were tested for their suitability. The most accurate result was obtained using the NN with an optimized one-month lead time approach (lead times varied between one and six months for the different input variables).

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A neural network model for short term river flow prediction

Cited by 28 publications

References 10 publications

Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

Predicting summer rainfall in the Yangtze River basin with neural networks

Neural Network Based Seasonal Predictions of Lake-Effect Snowfall

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