Prediction Of Suspended Sediment Concentration Research Articles

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Applying a machine learning-based method for the prediction of suspended sediment concentration in the Red river basin

Applying a machine learning-based method for the prediction of suspended sediment concentration in the Red river basin

Daily suspended sediment concentration forecast in the upper reach of Yellow River using a comprehensive integrated deep learning model

The precise prediction of suspended sediment concentration (SSC) is of great importance for river reservoir construction planning, water resource management, and ecological environment restoration. This research aims to improve SSC prediction accuracy by constructing a comprehensive and integrated deep learning model Wavelet-MGGP-CNN-LSTM (ICNN-LSTM), combining wavelet transformation (WT), multi-gene genetic programming (MGGP), convolutional neural network (CNN), and long short-term memory (LSTM) simultaneously. In ICNN-LSTM, the WT decomposes the signal and extracts time and frequency domain information, while the MGGP filters out redundant information. Then, the CNN and LSTM are integrated in a parallel and loosely coupled manner to form an initial combined model CNN-LSTM (CNN combined with LSTM) to process filtered information by WT and MGGP. Furthermore, this study compares the performance of ICNN-LSTM with CNN, LSTM, CNN-LSTM, ICNN (CNN embedded with WT and MGGP), ILSTM (LSTM embedded with WT and MGGP), artificial neural network (ANN), and the traditional sediment rating curve (SRC). The evaluation of prediction accuracy for all models was conducted using root mean square error (RMSE), Nash-Sutcliffe coefficient (NSC), coefficient of determination (R2), and mean absolute error (MAE) as performance indicators. The daily discharge and suspended sediment concentration series data from Tangnaihai Hydrological Station in the upper reaches of the Yellow River spanning from 1977 to 1987 were selected to train and test the models. Results show that, first, deep learning networks such as CNN and LSTM outperform the shallow neural network ANN, with LSTM providing higher accuracy than CNN. Second, the CNN-LSTM hybrid outperforms both CNN and LSTM models, exhibiting a nearly 89% improvement in NSC value compared to SRC in the test phase. Third, deep learning models such as ICNN, ILSTM, and ICNN-LSTM show significantly higher NSC values than CNN, LSTM, and CNN-LSTM models in the test phase, with improvements of 13.8%, 5.7%, and 12.1%, respectively. Moreover, compared to SRC, the proposed ICNN-LSTM model improves NSC value by nearly 140% in the test phase. The proposed ICNN-LSTM model, integrating the advantages of WT, deep learning, and ensemble learning, provides accurate and reliable predictions and serves as a reference for time series prediction modeling.

Online Control of the Raw Water System of a High-Sediment River Based on Deep Reinforcement Learning

Water supply systems that use rivers with high sedimentation levels may experience issues such as reservoir siltation. The suspended sediment concentration (SSC) of rivers experiences interannual variation and high nonlinearity due to its close relationship with meteorological factors, which increase the mismatch between the river water source and urban water demand. The raw water system scheduling problem is expressed as a reservoir and pump station control problem that involves real-time SSC changes. To lower the SSC of the water intake and lower the pumping station’s energy consumption, a deep reinforcement learning (DRL) model based on SSC prediction was developed. The framework consists of a DRL model, a hydraulic model for simulating the raw water system, and a neural network for predicting river SSC. The framework was tested using data from a Yellow River water withdrawal pumping station in China with an average capacity of 400,000 m3/d. The strategy created in this study can reduce the system energy consumption per unit of water withdrawal by 8.33% and the average annual water withdrawal SSC by 37.01%, when compared to manual strategy. Meanwhile, the deep reinforcement learning algorithm had good response robustness to uncertain imperfect predictive data.

Suspended sediment concentration prediction using long short-term memory (LSTM) networks at the Vu Gia Thu Bon catchment, Viet Nam

Understanding changes in sediment dynamics can significantly impact the sustainable development of the world. Modelling suspended sediment plays a crucial role because it allows decision makers and managers to formulate solutions that can avert the blockage of the sediments in a watershed. Estimates of suspended sediment is useful for global ecosystem service assessments and identify vulnerable aspects under climate change. In this study, it is suggested that the suspended sediment concentration at the Vu Gia-Thu Bon catchment in Vietnam be predicted using Long Short-Term Memory (LSTM) networks. Both the monthly suspended sediment concentration at the Thanh My station and the monthly runoff data are inputs. The findings compare the actual data to the predicted concentrations of suspended sediment. The training and testing sets use monthly data from 1978 to 2005 and from 2006 to 2019, respectively. To determine the best LSTM model for the catchment, the research analyzes different scenarios with various hyperparameters. The contrast demonstrates that the LSTM model can forecast suspended sediment concentration time series at the catchment.

Prediction of suspended sediment concentration in the lower Yellow River in China based on the coupled CEEMD-NAR model.

The scientific and accurate prediction of suspended sediment concentrations is of great importance for river management in the lower reaches of the Yellow River and for the scheduling of water conservancy projects in the upper and middle reaches. In order to solve the influence of the non-linear and non-smooth characteristics of the suspended sediment concentration series in the lower Yellow River on the prediction results and improve the prediction accuracy, this paper proposes a coupled model based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and non-linear autoregressive (NAR) model. Take the predicted suspended sediment concentrations in the lower reaches of the Yellow River at the Huayuankou hydrographic station as an example. The accuracy and stability of the coupled CEEMD-NAR model were verified through the Gaocun and Lijin hydrological stations. The CEEMD-NAR model predicted suspended sediment concentrations with a Nash-Sutcliffe efficiency (NSE) factor of 0.93. The three statistical evaluation indicators of the CEEMD-NAR model, mean absolute error (MAE), mean relative error (MRE), and root mean square error (RMSE) were 2.12kg/m3, 1.07, and 3.75kg/m3 respectively. In contrast to the NAR, EMD-NAR, and EEMD-NAR models, the coupled CEEMD-NAR model has good stability and high prediction accuracy and can be used in non-linear, non-smooth suspended sediment concentration long series prediction.

Applying the C-Factor of the RUSLE Model to Improve the Prediction of Suspended Sediment Concentration Using Smart Data-Driven Models

The accurate forecasts and estimations of the amount of sediment transported by rivers are critical concerns in water resource management and soil and water conservation. The identification of appropriate and applicable models or improvements in existing approaches is needed to accurately estimate the suspended sediment concentration (SSC). In recent decades, the utilization of intelligent models has substantially improved SSC estimation. The identification of beneficial and proper input parameters can greatly improve the performance of these smart models. In this regard, we assessed the C-factor of the revised universal soil loss equation (RUSLE) as a new input along with hydrological variables for modeling SSC. Four data-driven models (feed-forward neural network (FFNN); support vector regression (SVR); adaptive neuro-fuzzy inference system (ANFIS); and radial basis function (RBF)) were applied in the Boostan Dam Watershed, Iran. The cross-correlation function (CCF) and partial autocorrelation function (PAFC) approaches were applied to determine the effective lag times of the flow rate and suspended sediment, respectively. Additionally, several input scenarios were constructed, and finally, the best input combination and model were identified through trial and error and standard statistics (coefficient of determination (R2); root mean square error (RMSE); mean absolute error (MAE); and Nash–Sutcliffe efficiency coefficient (NS)). Our findings revealed that using the C-factor can considerably improve model efficiency. The best input scenario in which the C-factor was combined with hydrological data improved the NS by 16.4%, 21.4%, 0.17.5%, and 23.2% for SVR, ANFIS, FFNN, and RBF models, respectively, compared with the models using only hydrological inputs. Additionally, a comparison among the different models showed that the SVR model had about 4.1%, 13.7%, and 23.3% (based on the NS metric) higher accuracy than ANFIS, FFNN, and RBF for SSC estimation, respectively. Thus, the SVR model using hydrological data along with the C-factor can be a cost-effective and promising tool in SSC prediction at the watershed scale.

Capability assessment of conventional and data-driven models for prediction of suspended sediment load.

Information about suspended sediment concentration (SSC) in the stream is vital for sustainability of water conservation and erosion control planning, designing and monitoring. In this research, prediction of SSC has been done using artificial neural network (ANN), support vector regression (SVR) and multi-linear regression (MLR) models. Performance evaluation of developed models has been carried out on the basis of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (CE) and pooled average relative error (PARE). Cross-correlation function (CCF) validated that gamma test (GT) is an appropriate tool for the selection of most responsive input variables. On the basis of GT and CCF, GT-6 model was selected as the model with most effective input variables, with the values of gamma, standard error and V-ratio as 0.0643, 0.00583 and 0.2570, respectively. The ANN (6-3-1) model performed better than the other single and double hidden layered ANN models with the values of r, RMSE, CE and PARE as 0.939, 0.0063g/l, 85.17 and 0.0160, respectively. The performance of the SVR model was found better with the values of r, RMSE, CE and PARE as 0.906, 0.018g/l, 79.09 and 0001, respectively, but slightly poor than the selected ANN (6-3-1) model. The values of r, RMSE, CE and PARE were found as 0.899, 0.0312g/l, 65.15 and - 0.0031, respectively, in the case of MLR model. The present study revealed that among the ANN, SVR and MLR models, the ANN model with a single hidden layer is most suitable for observed SSC. The present study offers the simple efficient model to estimate the suspended sediment concentration in the stream with minimum error using limited data set.

Comparison of Bayesian, k-Nearest Neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction

Suspended sediment transport in river system is a complex process influenced by many factors that their interactions lead to nonlinear and high scatter of concentration-discharge relationships. This makes the model prediction subject to high uncertainty and providing one value as the model prediction is somehow useless and cannot provide adequate information about the model accuracy and associated uncertainty. Current study compares the efficiency of Bayesian (i.e. Bayesian segmented linear regression (BSLR) and Bayesian linear model (BLR)), Gaussian Process Regression (GPR) and k-Nearest Neighbor (k-NN) in quantifying uncertainty of the suspended sediment concentration prediction in three watersheds namely Arazkoseh, Oghan and Jajrood located in Iran. Three input combinations including, contemporary discharge, slow and quick flow components and contemporary, one and two antecedent days discharge, were used. The BSLR model was able to identify threshold value, furthermore, pre-threshold and post-threshold slopes of BSLR model indicated that for Arazkoseh watershed channel and for Oghan and Jajrood watersheds, upland area are dominate sediment sources. In all three studied cases, given prediction interval width and the percent of enclosed observed data by prediction interval, k-NN model provided more reliable prediction interval. Moreover, separation stream flow into slow and quick flow components lead to improved performance of GPR and k-NN models in the studied watersheds, and the best results for Arazkoseh and Oghan watersheds were obtained when slow and quick flow components were used as the model input.

Integration of ANFIS with PCA and DWT for daily suspended sediment concentration prediction

Quantifying sediment load is vital for aquatic and riverine biota and has been the subject of various environmental studies since sediment plays a key role in maintaining ecological integrity, river morphology and agricultural productivity. However, predicting sediment concentration in rivers is difficult because of the non-linear relationships of flow rates, geophysical characteristics and sediment loads. It is thus very important to propose suitable statistical methods which can provide fast, accurate and robust prediction of suspended sediment concentration (SSC) for management guidance. In this study, we developed coupled models of discrete wavelet transform (DWT) with adaptive neuro-fuzzy inference system (ANFIS), named DWT-ANFIS, and principal component analysis (PCA) with ANFIS, named PCA-ANFIS, for SSC time-series modeling. The coupled models and single ANFIS model were trained and tested using long-term daily SSC and river discharge which were measured on the Schuylkill and Iowa Rivers in the United States. The findings showed that the PCA-ANFIS performed better than the single ANFIS and the coupled DWT-ANFIS. Further applications of the PCA-ANFIS should be considered for simulation and prediction of other indicators relating to weather, water resources, and the environment.

Prediction of suspended sediment concentration using hybrid SVM-WOA approaches

Suspended sediment concentration (SSC) is one of the primary reasons with respect to watersheds or river basins, which must be assessed in a correct manner so that it will help decision makers to make right decisions regarding hydraulic structure, flash-flood, flood-mitigation of the basin. The present research evaluated efficacy of a hybrid model integrating Support Vector Machine with Whale optimization algorithm (SVM-WOA) for predicting SSC at Sundargarh and Salebhata stations in Mahanadi River, India. Various quantitative statistical evaluation constrains are applied to evacuate the model performance. Also, model performance of SVM-WOA is compared with SVM-PSO (Particle Swarm Optimization) and conventional SVM and RBFN (Radial Basis Function Network) models. The results reveal that, SVM-WOA performed superiorly in comparison to SVM-PSO, SVM and RBFN models for five different input scenarios during both training and testing phases. Hence, it is recommended to apply SVM-WOA as an appropriate technique for hydrological simulation at the basin.

Suspended sediment load prediction using long short-term memory neural network

Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability

In situ observations of suspended sediment concentration (SSC) and hydrodynamics were conducted in the subaqueous Yellow River Delta, China. With the dataset, a new least absolute shrinkage and selection operator (LASSO) regression model with temporal autocorrelation incorporated (temporal LASSO) is proposed for SSC prediction and mechanism investigation in coastal oceans. The model is concise and practical, effectively shrinking the interrelated variables into representative ones, while also achieving one-hour ahead forecasting with both higher accuracy and better interpretability than other data-driven methods. The model interpretability is further validated with direct data analysis from a physical perspective. Specifically, Empirical Mode Decomposition is employed to decouple the measured SSC into intrinsic mode functions (IMFs) and a residual. The periods of each subseries estimated from both zero-crossing and spectrum analysis show that IMF1 physically corresponds to the sediment resuspension by M4 tidal currents, IMF2 is the M2 tidal advection, IMF3-IMF5 are the resuspension by wind waves, IMF6 is the spring–neap tidal pumping of sediments. The contributions estimated with the ratio of variance are 12 %, 14 %, 63 %, and 10 %, respectively, over the observation period. The residual is the seasonal variations which can be taken as the background SSC thus not included for variance contribution. Waves make the dominant contribution which verifies the rationality of the LASSO shrinkage and confirms the model interpretability. The temporal LASSO model is shown to be a potential tool for emergency forecasting and mechanism explanation of SSC to benefit ocean environmental engineering management.

Effectiveness of heuristic approach for daily sediment flow prediction of Koyna river basin

The aim of this study was to test the applicability of heuristic approach for daily suspended sediment concentration (SSC) prediction of Koyna river basin situated in western Maharashtra region of India. Daily rainfall, runoff and SSC data were used for development of different models. Heuristic based models like artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), statistical model like multiple linear regressions (MLR) and traditional sediment rating curve (SRC) models were developed for SSC prediction. In order to avoid time consuming and tedious trial and error method by varying inputs, Gamma test was applied to select best inputs for development of different SSC prediction models. 39 ANN models were developed using tansig, logsig and purelin transfer function. 48 ANFIS models were developed using subtractive clustering method of FIS generation. The best model was selected on the basis performance criteria such as higher correlation coefficient (r) and coefficient of determination (R2). It was found that ANN (6-7-1) model with purelin transfer function outperformed all other models with r and R2 values of 0.79 and 0.62, respectively for daily SSC prediction. The traditional SRC model performed worst and could not able to catch the overall shape of observed SSC time series. The coefficient of determination (R2) was increased from 0.42 to 0.62 using heuristic approach than traditional SRC method. It was found that the heuristic approach could be successfully applied for cumulative SSC load estimation which is essential in reservoir operations and managements. It proves the applicability of heuristic approach in hydrologic modelling for better watershed planning and management.

Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales

The monitoring of hydro-sedimentological processes is important for environmental control but depends on resources that are not always available. The estimation of sedimentological variables with mathematical models is often limited by the scarcity of data for a single basin. This research experiments with the simulation of suspended sediment concentration (SSC) and turbidity (T) using a regional model, with data from agricultural basins of different scales within the same hydrographic region, using hourly precipitation as one of the predictive variables, aggregated through the exponentially weighted moving average (EWMA) of past rainfall, in artificial neural network (ANN) and fuzzy inference system (FIS) models. The data monitoring was performed from January 2013 to February 2020 in four watersheds within the same region in southern Brazil, with areas ranging from 1.3 to 524.3 km2. For the turbidity estimation, the FIS model, which also made use of the discharge (Q) and area (A) of each basin as inputs, performed best, with a Nash-Sutcliffe efficiency (NS) of 0.860 for the verification samples. Several FIS and ANN models performed very well for SSC prediction (with NSs ranging from 0.950 to 0.977) due to the EWMA variable, including an FIS model that uses only this variable (NS 0.952). The results allow us to conclude that it is possible, with few data for the individual basin and a regional empirical model, to estimate SSC and turbidity, provided the aggregation of hourly precipitation by the EWMA, as long as the basins have similar physical and climatic characteristics.

ANN modeling of the complex discharge-sediment concentration relationship in Bhagirathi river basin of the Himalaya

A simple technique for prediction of suspended sediment concentration (SSC) or total suspended matter in the Himalayan Bhagirathi river is presented. Artificial neural network models have been developed using short time period data of discharge (Q) and SSC during the high activity monsoon period of June to October 2004, when variations are maximum. Two modeling approaches have been employed, a daily approach and a three hourly approach. Although the time period considered is the same in both the approaches, the modeling performance is marginally better in the three hourly approaches where there is a sixfold increase in the data set. The Levenberg–Marquardt optimization algorithm has been improved with NARX [nonlinear autoregressive with exogenous input] architecture and high values of coefficient of determination have been obtained [0.89–0.97]. This study shows that short duration time series data can be used for successfully predicting geo-hydrological variables in the highly complex Himalayan river scenario.

Suspended sediment concentration forecast based on CEEMDAN-GRU model

Abstract Reasonable prediction of suspended sediment concentration is of great significance for river regulation and water conservancy project scheduling in upper and middle reaches. In order to solve the influence of nonlinear and non-stationary characteristics of sediment sequences on the prediction results and improve the prediction accuracy, a prediction model of sediment based on CEEMDAN-GRU was constructed. The monthly suspended sediment concentration data of Huayuankou hydrological station from 1960 to 2014 were ‘decomposed-predicted-reconstructed’ and compared with single gated recurrent unit (GRU), support vector machine (SVM) and long short-term memory (LSTM) model. The results reveal that the CEEMDAN-GRU coupling model has provided a superior alternative to the single model and its determination coefficients (DC) of the training set and testing set are greater than 0.71, qualified rate (QR) reaches up to 81%, average absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) are 1.2841, 0.9675 and 4.3560 respectively. It is proved that the CEEMDAN-GRU model has a better performance and can be used in the mid- and long-term prediction of non-linear and non-stationary suspended sediment concentration series.

Estimating Impacts of Dam Development and Landscape Changes on Suspended Sediment Concentrations in the Mekong River Basin’s 3S Tributaries

The Mekong River Basin (MRB) is undergoing rapid dam development, which is altering the river suspended sediment concentration (SSC). In this study, we used satellite remote sensing records spanning 31 years to detect SSC changes (SSC prediction r2=0.78, RMSE=21.2 mg/L) due to dam development. We focused on the 3S basin of the MRB. We also used satellite data on nighttime lights, which reflect human settlement patterns, and land cover to explain SSC patterns. Our technique allowed for quantification of SSC changes due to dam construction (e.g., +120 mg/L near basin outlet), reservoir sediment trapping (e.g., −108 mg/L), deforestation, and human settlement (e.g., +117 mg/L near impacts). Our technique also demonstrated how the SSC of the 3S rivers compared to that of the Mekong mainstem over time (e.g., from ∼13% to 100% greater). Our comprehensive analyses of SSC records with dam development indicate that SSC changes will continue with ongoing dam and landscape development in the MRB. From a hydrologic perspective, SSC monitoring will be imperative for effective sediment and water management. Our satellite-based approach answers critical sediment needs of improved monitoring and adaptive management throughout the MRB and other global locations for practitioners who are engaged in real-world management of their river basins.

Long short-term memory for predicting daily suspended sediment concentration

Frequent and accurate estimation of suspended sediment concentration (SSC) in surface waters and hydraulic schemes is of prime importance for proper design, operation and management of many hydraulic projects. in the present study, a long short-term memory (LSTM) was considered for predicting daily suspended sediment concentration in a river. The LSTM extends recurrent neural network with memory cells, instead of recurrent units, to store and output information, easing the learning of temporal relationships on long time scales. To build the model, daily observed time series of river discharge (Q) and SSC in the Schuylkill River in the United States were used. The results of the proposed model were evaluated and compared with the feedforward neural network and the adaptive neuro fuzzy inference system models which were trained using three different learning algorithms and widely used in the literature for prediction of daily SSC. The comparison of prediction accuracy of the models demonstrated that the LSTM model could satisfactory predict SSC time series, and adequately estimate cumulative suspended sediment load (SSL).

Evaluating the performance of four different heuristic approaches with Gamma test for daily suspended sediment concentration modeling.

Accurate prediction of suspended sediment concentration (SSC) carried by a river or watershed basin is essential for understanding the hydrology of basin in terms of water quality, river bed sustainability and aquatic habitats. In this study, four heuristic methods, namely, radial basis neural network (RBNN), self-organizing map neural network (SOMNN), least square support vector regression (LSSVR), and multivariate adaptive regression spline (MARS) were employed for daily SSC modeling at Ashti, Bamini, and Tekra stations located in Godavari River basin, Andhra Pradesh, India. The Gamma test (GT) was utilized for identifying the most significant input variables for the applied heuristic approaches. The results obtained by RBNN, SOMNN, LSSVR, and MARS models were compared with those of the traditional sediment rating curve (SRC). The performance of the models was evaluated based on the root mean square error (RMSE), coefficient of efficiency (COE), Pearson correlation coefficient (PCC), Willmott index (WI), and pooled average relative error (PARE) indices, as well as the visual inspection using line diagram, scatter diagram, and Taylor diagram (TD). The results of comparison revealed that the four heuristic methods gave higher accuracy than the SRC model. Among the heuristic models, the RBNN-3 (RMSE = 0.045, 0.062, 0.131g/l; COE = 0.884, 0.883, 0.914; PCC = 0.955, 0.961, 0.958; and WI = 0.970, 0.963, 0.976) outperformed the other models in simulating daily SSC records in the studied stations.