A comparison between neural-network forecasting techniques-case study: river flow forecasting

1999 ◽  
Vol 10 (2) ◽  
pp. 402-409 ◽  
Author(s):  
A.F. Atiya ◽  
S.M. El-Shoura ◽  
S.I. Shaheen ◽  
M.S. El-Sherif
Keyword(s):  
Neural Network ◽  
River Flow ◽  
River Flow Forecasting ◽  
Forecasting Techniques

scholarly journals Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3049
Author(s):  
Chiara Belvederesi ◽  
John Albino Dominic ◽  
Quazi K. Hassan ◽  
Anil Gupta ◽  
Gopal Achari
Keyword(s):  
River Flow ◽  
Cold Weather ◽  
Difference Model ◽  
Short Term ◽  
Athabasca River ◽  
Explanatory Variables ◽  
Climatic Regions ◽  
River Flow Forecasting ◽  
Modelling Techniques

Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (ENS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle.

scholarly journals Improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps

2012 ◽  
Vol 15 (2) ◽  
pp. 486-502 ◽  
Author(s):  
Mukesh K. Tiwari ◽  
Ki-Young Song ◽  
Chandranath Chatterjee ◽  
Madan M. Gupta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Lead Time ◽  
River Flow ◽  
Simulation Studies ◽  
River Flow Forecasting ◽  
Testing Dataset ◽  
Crucial Problem ◽  
Daily Discharge ◽  
Selection Of

Neural network (NN) models have gained much attention for river flow forecasting because of their ability to map complex non-linearities. However, the selection of appropriate length of training datasets is crucial and the uncertainty in predictions of the trained NNs with new datasets is a crucial problem. In this study, self-organising maps (SOM) are used to classify the datasets homogeneously and the performance of four types of NN models developed for daily discharge predictions – namely traditional NN, wavelet-based NN (WNN), bootstrap-based NN (BNN) and wavelet-bootstrap-based NN (WBNN) – is analysed for their applicability cluster-wise. SOM classified the training datasets into three clusters (i.e. cluster I, II and III) and the trained SOM is then used to assign testing datasets into these three clusters. Simulation studies show that the WBNN model performs better for the entire testing dataset as well as for values in clusters I and III; for cluster II the performance of BNN model is better compared with others for a 1-day lead time forecasting. Overall, it is found that the proposed methodology can enhance the accuracy and reliability of river flow forecasting.

scholarly journals River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin

2009 ◽  
Vol 13 (9) ◽  
pp. 1607-1618 ◽  
Author(s):  
M. K. Akhtar ◽  
G. A. Corzo ◽  
S. J. van Andel ◽  
A. Jonoski
Keyword(s):  
Travel Time ◽  
River Flow ◽  
Ganges River ◽  
River Flow Forecasting ◽  
Processing Step ◽  
Flow Length ◽  
Spatially Distributed ◽  
Ann Models ◽  
The Ganges

Abstract. This paper explores the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow forecasting. Spatially distributed precipitation is commonly required when modelling large basins, and it is usually incorporated in distributed physically-based hydrological modelling approaches. However, these modelling approaches are recognised to be quite complex and expensive, especially due to the data collection of multiple inputs and parameters, which vary in space and time. On the other hand, ANN models for flow forecasting are frequently developed only with precipitation and discharge as inputs, usually without taking into consideration the spatial variability of precipitation. Full inclusion of spatially distributed inputs into ANN models still leads to a complex computational process that may not give acceptable results. Therefore, here we present an analysis of the flow length and travel time as a basis for pre-processing remotely sensed (satellite) rainfall data. This pre-processed rainfall is used together with local stream flow measurements of previous days as input to ANN models. The case study for this modelling approach is the Ganges river basin. A comparative analysis of multiple ANN models with different hydrological pre-processing is presented. The ANN showed its ability to forecast discharges 3-days ahead with an acceptable accuracy. Within this forecast horizon, the influence of the pre-processed rainfall is marginal, because of dominant influence of strongly auto-correlated discharge inputs. For forecast horizons of 7 to 10 days, the influence of the pre-processed rainfall is noticeable, although the overall model performance deteriorates. The incorporation of remote sensing data of spatially distributed precipitation information as pre-processing step showed to be a promising alternative for the setting-up of ANN models for river flow forecasting.

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