The science of the total environment

Estimates of sediment load are required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods for simulating the suspended sediment load. In this study artificial neural networks (ANNs) are employed to estimate the daily total suspended sediment load on rivers. Two different ANN algorithms, the feed-forward back-propagation (FFBP) method and the radial basis functions (RBF), were used for this purpose. The neural networks are trained using rainfall flow and suspended sediment load data from the Juniata Catchment, USA. The simulations provided satisfactory simulations in terms of the selected performance criteria comparing well with conventional multi-linear regression. Similarly, the simulated sediment load hydrographs obtained by two ANN methods are found closer to the observed ones again compared with multi-linear regression.

Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data

Suspended sediment modeling using genetic programming and soft computing techniques

The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r^2=0.98 and RMSE=0.002431) compared to the existing predictor.

Short communication: ANFIS-based approach for predicting sediment transport in clean sewer

Modeling sediment load is a significant factor in water resources engineering as it affects directly the design and management of water resources. In this study, artificial neural networks (ANNs) are employed to estimate the daily sediment load. Two different ANN algorithms, the feed forward neural network (FFNN) and radial basis function (RBF) have been used for this purpose. The neural networks are trained and tested using daily sediment and flow data from Rantau Panjang station on Johor River. The results show that combining flow data with sediment load data gives an accurate model to predict sediment load. The comparison of the results indicate that the FFNN model has superior performance than the RB model in estimating daily sediment load.

ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

The methods available for sediment concentration and flux estimation are largely empirical, with sediment rating curves being the most widely applied. In this study, a comparison is made between artificial neural networks (ANNs) and sediment rating curves for two rivers with very similar catchment areas and characteristics in the north of England. Data from one river are used to estimate sediment concentrations and flux in the other for both estimation techniques. A more traditional, split-sample approach is also used, in which part of the available data from a site is used to develop a predictive relationship, which is then tested with the remaining data from the same site. The results of the two estimation techniques and the two approaches for the derivation of predictive capability are compared and discussed. The potential advantages of ANNs in sediment concentration and flux estimation are highlighted. In particular, an ANN approach can give information about the structure of events (e.g., hysteresis in the sediment concentration - water discharge relationship, and the effect of antecedent conditions) which is impossible to achieve with sediment rating curves.

https://journals.tubitak.gov.tr/engineering/issues/muh-02-26-1/muh-26-1-4-0010-6.pdf

Suspended Sediment Estimation for Rivers using Artificial Neural Networks and Sediment Rating Curves

The methods available in the literature for sediment concentration estimation are complicated and time consuming and necessitate cumbersome parameter estimation procedures. In this study, artificial neural networks (ANNs) are used to forecast and estimate sediment concentration values. The forecasting results obtained using previously observed sediment values were close to the real ones. The sediment concentration estimation, on the other hand, using only observed river flow values and the previous sediment value in a nearby river as input, provided realistic approximations in terms of mean squared error (MSE) and total sediment amount. The ANN estimates are compared also with corresponding classical regression ones and found to be significantly superior.

/pdf/suspended-sediment-estimation-and-forecasting-using-4fznp1ofk7.pdf

Hikmet Kerem Ciğizoğlu

Papers

Suspended Sediment Estimation for Rivers using Artificial Neural Networks and Sediment Rating Curves

Suspended sediment estimation and forecasting using artificial neural networks