Short term streamflow forecasting using artificial neural networks

  title={Short term streamflow forecasting using artificial neural networks},
  author={Cameron M Zealand and Donald H. Burn and Slobodan P. Simonovic},
  journal={Journal of Hydrology},
Application of Artificial Neural Networks for river flow simulation in three French catchments.
In a multi-model approach for deriving consensus forecasts, the NNM (as one of three Model Output Combination Techniques (MOCTs) considered) is found to be the best performing MOCT and also better than the best individual model.
Streamflow Forecasting Using Different Artificial Neural Network Algorithms
Four different ANN algorithms, namely, backpropagation, conjugate gradient, cascade correlation, and Levenberg–Marquardt are applied to continuous streamflow data of the North Platte River in the United States and the results are compared with each other.
Reservoir inflow forecasting using artificial neural network
An Artificial Neural Network approach for forecasting of long term reservoir inflow using monthly inflow available data using Levenberg-Marquardt Back Propagation algorithm has been presented.
An assessment of multi-layer perceptron networks for streamflow forecasting in large-scale interconnected hydrosystems
This work analyzes the use of artificial neural networks in the short-term streamflow forecasting for large interconnected hydropower systems. The state-of-the-art optimization algorithms, activation
Comparison of different ANN techniques in river flow prediction
In general, the forecasting performance of RBF is found to be superior to the other two ANN techniques and a time series model in terms of the selected performance criteria.
Artificial Neural Networks In Water Resources
The forecasting performance of ANN techniques is found to be superior to the other conventional statistical and stochastic methods in terms of the selected performance criteria.
Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks
Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation.
River flow forecasting and estimation using different artificial neural network techniques
This paper demonstrates the application of different artificial neural network (ANN) techniques for the estimation of monthly streamflows. In the first part of the study, three different ANN
Evaluation of Neural Network Streamflow Forecasting on 47 Watersheds
This study is designed to compare 1 day ahead streamflow forecasting performance of multiple-layer perceptron (MLP) networks implemented at a daily time step for 47 watersheds spread across France


The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters
This paper presents the use of artificial neural networks (ANNs) as a viable means of forecasting water quality parameters. A review of ANNs is given, and a case study is presented in which ANN
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
An artificial neural network (ANN) is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. ANN models have been found
Neural Networks for River Flow Prediction
This paper demonstrates how a neural network can be used as an adaptive model synthesizer as well as a predictor in the flow prediction of the Huron River at the Dexter sampling station, near Ann Arbor, Mich.
Neural nets for modelling rainfall-runoff transformations
To obtain river flow data, a neural network (NN) is developed and applied to rainfall-runoff transformation. The NN has been built considering a hidden two layer net and the sigmoidal has been used
Deriving a General Operating Policy for Reservoirs Using Neural Network
Reservoir operating policies are derived to improve the operation and efficient management of available water for the Aliyar Dam in Tamil Nadu, India, using a dynamic programming (DP) model, a
Neural-Network Models of Rainfall-Runoff Process
Spatially distributed rainfall patterns can now be detected using a variety of remote–sensing techniques ranging from weather radar to various satellite–based sensors. Conversion of the remote–sensed
ABSTRACT: By employing a set of criteria for classifying the capabilities of time series models, recent developments in time series analysis are assessed and put into proper perspective. In
Effective and efficient global optimization for conceptual rainfall‐runoff models
The successful application of a conceptual rainfall-runoff (CRR) model depends on how well it is calibrated. Despite the popularity of CRR models, reports in the literature indicate that it is