Book ChapterDOI
Suspended Sediment Estimation Using an Artificial Intelligence Approach
Mustafa Demirci,Fatih Üneş,Sebahattin Saydemir +2 more
- pp 83-95
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TLDR
In this article, a neural network approach is proposed to predict suspended sediment concentration from streamflow, and a comparison was performed between artificial neural network, sediment rating-curve and multilinear regression models.Abstract:
Forecasting of sediment concentration in rivers is a very important process for water resources assignment development and management. In this paper, a neural network approach is proposed to predict suspended sediment concentration from streamflow. A comparison was performed between artificial neural network, sediment rating-curve and multilinear regression models. It was based on a 5 years period of continuous streamflow, suspended sediment concentration and mean water temperature data of West Virginia, Little Coal River, Danville station operated by the United States Geological Survey. Based on comparison of the results, it is found that the artificial neural network model gives better estimates than the sediment rating-curve and multilinear regression techniques.read more
Citations
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Journal ArticleDOI
Impact of Normalization and Input on ARMAX-ANN Model Performance in Suspended Sediment Load Prediction
Hamid Moeeni,Hossein Bonakdari +1 more
TL;DR: A new hybrid model comprising two commonly used stochastic and nonlinear models is introduced comprising an autoregressive-moving average with exogenous terms (ARMAX) and an artificial neural network (ANN).
Journal ArticleDOI
Estimating Dam Reservoir Level FluctuationsUsing Data-Driven Techniques
TL;DR: In this paper, an adaptive network-based fuzzy inference system (ANFIS), support vector machines (SVM), radial basis neural networks (RBNN), and generalized regression neural network (GRNN) approaches were used for the prediction and estimation of daily reservoir levels of Millers Ferry Dam on the Alabama River in the USA.
Journal ArticleDOI
Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach
TL;DR: In this article, Artificial Neural Networks (ANN), M5tree (M5T) approaches and statistical approaches such as Multiple Linear Regression (MLR), Sediment Rating Curves (SRC) are used for estimation daily suspended sediment concentration from daily temperature of water and streamflow in river.
Journal ArticleDOI
Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria
TL;DR: In this article, the authors presented the performance of the best training algorithm in multilayer perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment.
Journal ArticleDOI
Two decades on the artificial intelligence models advancement for modeling river sediment concentration: State-of-the-art
Taher Rajaee,Hamideh Jafari +1 more
TL;DR: This study showed that AI models could be successfully employed to simulate the complex river sediment process in situations where explicit knowledge of internal sub-process is not required and an improvement in the prediction accuracy through the use of hybrid models.
References
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Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal Article
Short Term Streamflow Forecasting Using Artificial Neural Networks
TL;DR: The research described in this article investigates the utility of Artificial Neural Networks for short term forecasting of streamflow and compares the performance of this tool to conventional approaches used to forecast streamflow.
Related Papers (5)
Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches
Mustafa Demirci,Ahmet Baltaci +1 more