River suspended sediment load prediction based on river discharge information: application of newly developed data mining models

ABSTRACT

Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process.     

Comparison of local and global approximators in multivariate chaotic forecasting of daily streamflow

ABSTRACT

Although it is conceptually assumed that global models are relatively ineffective in modelling the highly unstable structure of chaotic hydrologic dynamics, there is not a detailed study of comparing the performances of local and global models in a hydrological context, especially with new emerging machine learning models. In this study, the performance of a local model (k-nearest neighbour, k-nn) and, as global models, several recent machine learning models – artificial neural network (ANN), least square-support vector regression (LS-SVR), random forest (RF), M5 model tree (M5), multivariate adaptive regression splines (MARS) – was analysed in multivariate chaotic forecasting of streamflow. The models were developed for Australia’s largest river, the River Murray. The results indicate that the k-nn model was more successful than the global models in capturing the streamflow dynamics. Furthermore, coupled with the multivariate phase-space, it was shown that the global models can be successfully used for obtaining reliable uncertainty estimates for streamflow.     

Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers

Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.     

Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed,India

Abstract

The process-based Soil and Water Assessment Tool (SWAT) model and the data-driven radial basis neural network (RBNN) model were evaluated for simulating sediment load for the Nagwa watershed in Jharkhand, India, where soil erosion is a severe problem. The SWAT model calibration and uncertainty analysis were performed with the Sequential Uncertainty Fitting algorithm version 2 and the bootstrap technique was applied on the RBNN model to analyse uncertainty in model output. The percentage of data bracketed by the 95% prediction uncertainty (95PPU) and the r factor were the two measures used to assess the goodness of calibration. Comparison of the results of the two models shows that the value of r factor (r = 0.41) in the RBNN model is less than that of SWAT model (r = 0.79), which means there is a wider prediction interval for the SWAT model results. More values of observed sediment yield were bracketed by the 95PPU in the RBNN model. Thus, the RBNN model estimates the sediment yield values more accurately and with less uncertainty.

Editor D. Koutsoyiannis; Associate editor H. Aksoy

Citation Singh, A., Imtiyaz, M., Isaac, R.K., and Denis, D.M., 2014. Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed, India. Hydrological Sciences Journal, 59 (2), 351–364.     

Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones

Abstract

The abilities of neuro-fuzzy (NF) and neural network (NN) approaches to model the streamflow–suspended sediment relationship are investigated. The NF and NN models are established for estimating current suspended sediment values using the streamflow and antecedent sediment data. The sediment rating curve and multi-linear regression are also applied to the same data. Statistic measures were used to evaluate the performance of the models. The daily streamflow and suspended sediment data for two stations—Quebrada Blanca station and Rio Valenciano station—operated by the US Geological Survey were used as case studies. Based on comparison of the results, it is found that the NF model gives better estimates than the other techniques.     

Application of soft-computing techniques in forecasting sediment load and concentration

ABSTRACT

This paper investigates conventional and soft-computing methods for the estimation of suspended sediment concentration (SSC) and load (SSL) in rivers. Frequently used methods of sediment rate curve (SRC) and multi-nonlinear regression, and soft-computing methods of multi-layer perceptron, multi-linear regression and adaptive neuro-fuzzy inference system are implemented using various hydrological and hydraulic parameters for the Little Kickapoo Creek Watershed, Illinois, USA. All methods performed equally well in the estimation of SSL, without any noticeable outperformance from any from the methods. However, the application of soft-computing methods decreased SSC estimation errors considerably as compared to the results of SRC. The results are significant in the way they reconcile traditionally used hydrological parameters into the soft-computing methods. Overall, soft-computing methods are recommended for the estimation of SSC in rivers because of their reasonably better performance and ease of implementation.     

Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt

Abstract

Abstract The prediction and estimation of suspended sediment concentration are investigated by using multi-layer perceptrons (MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and down-stream station sediment data, separately, and the second part focuses on the estimation of downstream suspended sediment data by using data from both stations. In each case, the MLP test results are compared to those of generalized regression neural networks (GRNN), radial basis function (RBF) and multi-linear regression (MLR) for the best-input combinations. Based on the comparisons, it was found that the MLP generally gives better suspended sediment concentration estimates than the other neural network techniques and the conventional statistical method (MLR). However, for the estimation of maximum sediment peak, the RBF was mostly found to be better than the MLP and the other techniques. The results also indicate that the RBF and GRNN may provide better performance than the MLP in the estimation of the total sediment load.     

Wavelet and Neuro‐fuzzy Conjunction Approach for Suspended Sediment Prediction

Simulation approaches employed in suspended sediment processes are important in the areas of water resources and environmental engineering. In the current study, neuro‐fuzzy (NF), a combination of wavelet transform and neuro‐fuzzy (WNF), multi linear regression (MLR), and the conventional sediment rating curve (SRC) models were considered for suspended sediment load (S) modeling in a gauging station in the USA. In the proposed WNF model, the discrete wavelet analysis was linked to a NF approach. To achieve this aim, the observed time series of river flow discharge (Q) and S were decomposed to sub time series at different scales by discrete wavelet transform. Afterwards, the effective sub time series were added together to obtain a useful Q and S time series for prediction. Eventually, the obtained total time series were imposed as inputs to the NF method for daily S prediction. The results illustrated that the predicted values by the proposed WNF model were in good agreement with the observed S values and gave better results than other models. Furthermore, the WNF model satisfactorily estimated the cumulative suspended sediment load and produced relatively reasonable predictions for extreme values of S, while NF, MLR, and SRC models provided unacceptable predictions.     

Modelling reservoir sedimentation and estimating historical deposition rates using a data-based mechanistic (DBM) approach

Abstract

The data-based mechanistic (DBM) modelling methodology is applied to the study of reservoir sedimentation. A lumped-parameter, discrete-time model has been developed which directly relates rainfall to suspended sediment load (SSL) at the reservoir outflow from the two years of measured data at Wyresdale Park Reservoir (Lancashire, UK). This nonlinear DBM model comprises two components: a rainfall to SSL model and a second model, relating the SSL at the reservoir inflow to the SSL at the reservoir spillway. Using a daily measured rainfall series as the input, this model is used to reconstruct daily deposition rates between 1911 and 1996. This synthetic sediment accretion sequence is compared with the variations in sand content within sediment cores collected from the reservoir floor. These profiles show good general agreement, reflecting the importance of low reoccurrence, high magnitude events. This preliminary study highlights the potential of this DBM approach, which could be readily applied to other sites.     

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.     

Discussion of “Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers”

Citation Abrahart, R.J. & Mount, N.J. (2011) Discussion of “Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers by S.A. Mirgagheri et al. (2010, Hydrol. Sci. J. 55(7), 1175–1189).” Hydrol. Sci. J. 56(7), 1325–1329.     

Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier

The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of suspended sediment loads in a seasonal stream in the wet‐dry tropics,Northern Territory,Australia

One year of instantaneous suspended sediment concentration, C, and instantaneous discharge, Q, data collected at Ngarradj downstream of the Jabiluka mine site indicate that the use of a simple C–Q rating curve is not a reliable method for estimating suspended sediment loads from the Ngarradj catchment. The C–Q data are not only complicated by hysteresis effects within the rising and falling stages of individual events, but also by variable depletion of available suspended sediment through multipeaked runoff events. Parameter values were fitted to an event‐based suspended sediment load–Q relationship as an alternative to the C–Q relationship. Total suspended sediment load and Q data for 10 observed events in the Ngarradj stream catchment were used to fit parameter values to a suspended sediment load–Q relationship, using (a) log–log regression and (b) iterative parameter fitting techniques. A more reliable and statistically significant prediction of suspended sediment load from the Ngarradj catchment is obtained using an event‐based suspended sediment load–Q relationship. Fitting parameters to the event‐based suspended sediment load–Q relationship using iterative techniques better predicts long‐term suspended sediment loads compared with log–log regression techniques. Copyright © 2004 John Wiley & Sons, Ltd.     

Estimation of suspended sediment concentration in the Saint John River using rating curves and a machine learning approach

ABSTRACT

Sedimentation in navigable waterways and harbours is of concern for many water and port managers. One potential source of variability in sedimentation is the annual sediment load of the river that empties in the harbour. The main objective of this study was to use some of the regularly monitored hydro-meteorological variables to compare estimates of hourly suspended sediment concentration in the Saint John River using a sediment rating curve and a model tree (M5?) with different combinations of predictors. Estimated suspended sediment concentrations were multiplied by measured flows to estimate suspended sediment loads. Best results were obtained using M5? with four predictors, returning an R² of 0.72 on calibration data and an R² of 0.46 on validation data. Total load was underestimated by 1.41% for the calibration period and overestimated by 2.38% for the validation period. Overall, the model tree approach is suggested for its relative ease of implementation and constant performance.

EDITOR M.C. Acreman; ASSOCIATE EDITOR B. Touaibia     

Discharge‐sediment processes of the Zhadang glacier on the Tibetan Plateau measured with a high frequency data acquisition system

In high elevation cold regions of the Tibetan Plateau, suspended sediment transfer from glacier meltwater erosion is one of the important hydrological components. The Zhadang glacier is a typical valley‐type glacier in the Nyainqentanglha Mountains on the Tibetan Plateau. To make frequent and long period records of meltwater runoff and sediment processes in the very high elevation and isolated regions, an automatic system was installed near the glacier snout (5400 m a.s.l) in August 2013, to measure the transient discharge and sediment processes at 5‐min interval, which is shorter than the time span for the water flow to traverse the catchment from the farthest end to the watershed outlet. Diurnal variations of discharge, and suspended sediment concentration (SSC) were recorded at high frequency for the Zhadang glacier, before suspended sediment load (SSL) was computed. Hourly SSC varied from the range of 0.2 kg/m³ to 0.5 kg/m³ (at 8:00–9:00) to the range of 2.0 kg/m³ to 4.0 kg/m³ (at 17:00–18:00). The daily SSL was 32.24 t during the intense ablation period. Hourly SSC was linearly correlated with discharge (r = 0.885**, n = 18, p < 0.01). A digit‐eight hysteresis loop was observed for the sediment transport in the glacier area. Air temperature fluctuations influence discharge, and then result in the sediment variations. The results of this study provide insight into the responses of suspended sediment delivery processes with a high frequency data in the high elevation cold regions. Copyright © 2016 John Wiley & Sons, Ltd.     

In situ and high frequency monitoring of suspended sediment properties using a spectrophotometric sensor

It is well known that sediment properties, including sediment‐associated chemical constituents and sediment physical properties, can exhibit significant variations within and between storm runoff events. However, the number of samples included in suspended sediment studies is often limited by time‐consuming and expensive laboratory procedures after stream water sampling. This restricts high frequency sampling campaigns to a limited number of events and reduces accuracy when aiming to estimate fluxes and loads of sediment‐associated chemical constituents. In this study, we address the potential of a portable ultraviolet–visible spectrophotometer (220–730 nm) to estimate suspended sediment properties in a resource efficient way. Several field deployable spectrophotometers are currently available for in‐stream measurements of environmental variables at high temporal resolution. These instruments have primarily been developed and used to quantify solute concentrations (e.g. dissolved organic carbon and NO₃‐N), total concentrations of dissolved and particulate forms (e.g. total organic carbon) and turbidity. Here we argue that light absorbance values can be calibrated to estimate sediment properties. We present light absorbance data collected at 15‐min intervals in the Weierbach catchment (NW Luxembourg, 0.45 km²) from December 2013 to January 2015. In this proof‐of‐concept study, we performed a local calibration using suspended sediment loss‐on‐ignition (LOI) measurements as an example of suspended sediment property. We assessed the performance of several regression models that relate light absorbance measurements with the percentage weight LOI. The MM‐robust regression method presented the lowest standard error of prediction (0.48%) and was selected for calibration (adjusted r² = 0.76 between observed and predicted values). The model was then used to predict LOI during a storm runoff event in December 2014. This study demonstrates that spectrophotometers can be used to estimate suspended sediment properties at high temporal resolution and for long‐time spans in a simple, non‐destructive and affordable manner. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques / Modélisation de la charge journalière en matières en suspension dans des rivières turques à l'aide de plusieurs techniques empiriques

Abstract

The transport of sediment load in rivers is important with respect to pollution, channel navigability, reservoir filling, longevity of hydroelectric equipment, fish habitat, river aesthetics and scientific interest. However, conventional sediment rating curves cannot estimate sediment load accurately. An adaptive neuro-fuzzy technique is investigated for its ability to improve the accuracy of the streamflow—suspended sediment rating curve for daily suspended sediment estimation. The daily streamflow and suspended sediment data for four stations in the Black Sea region of Turkey are used as case studies. A comparison is made between the estimates provided by the neuro-fuzzy model and those of the following models: radial basis neural network (RBNN), feed-forward neural network (FFNN), generalized regression neural network (GRNN), multi-linear regression (MLR) and sediment rating curve (SRC). Comparison of results reveals that the neuro-fuzzy model, in general, gives better estimates than the other techniques. Among the neural network techniques, the RBNN is found to perform better than the FFNN and GRNN.     

Long-range forecasting of suspended sediment

This study examines the employment of two methods, multiple linear regression (MLR) and an artificial neural network (ANN), for multistep ahead forecasting of suspended sediment. The autoregressive integrated moving average (ARIMA) model is considered for one-step ahead forecasting of sediment series in order to provide a comparison with the MLR and ANN methods. For one- and two-step ahead forecasting, the ANN model performance is superior to that of the MLR model. For longer ranges, MLR models provide better accuracy, but there is an important assumption violation. The Durbin-Watson statistics of the MLR models show a noticeable decrease from 1.3 to 0.5, indicating that the residuals are not dependent over time. The scatterplots of the three methods (MLR, ARIMA and ANN) for one-step ahead forecasting for the validation period illustrate close fits with the regression line, with the ANN configuration having a slightly higher R² value.     

Suspended load estimation using L1-fuzzy regression,L2-fuzzy regression and MARS-fuzzy regression models

ABSTRACT

The problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.

Editor D. Koutsoyiannis; Associate editor H. Aksoy