Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash-Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed\'s hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model\'s output. The results of SHAP showed that river discharge has the strongest impact on the model\'s output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model\'s output (DL model is as black-box model) are recommended in future research.

Suspended sediment (SS) is a natural component of aquatic environments. It is characterized by the adsorption of pollutants, and its physical properties can affect water volume quality. In this study, SS dynamics were simulated using a 2D hydrodynamic model in the Nanji Mountain Nature Reserve (NNR), and the fluxes of pollutants caused by SS were calculated to assess the biological risks during the wet (May-August) and dry (November-March) seasons. High spatial and temporal variability in SS load within the NNR was found in this study. The average SS load in the reserve increased and then decreased during the year, and the SS input from Ganjiang significantly affected the SS load in the NNR (p < 0.01). The SS load uptrend in the NNR occurred later than that of Ganjiang during the wet season because of the SS sedimentation in the NNR. And the suspension of SS in the NNR during the dry season resulted in a later SS load downtrend compared to Ganjiang. High SS load from Ganjiang during the wet season was responsible for the high nutrient and microplastic fluxes in the NNR, which were 8.38 and 10.61 times higher than those in the dry season, respectively. And the pollutant fluxes during the wet season were almost all from Ganjiang. In contrast, higher waterbird diversity and population during the dry season is the main reason for the increased biological risk of contaminants. Therefore, monitoring and managing SS and its contamination concentrations in rivers entering the lake is helpful for the protection of ecologically sensitive areas and key species in the lake.

The Sutlej River basin of the western Himalaya (study area), owing to its unique geographical disposition, receives precipitation from both the Indian summer monsoon (ISM) and the Westerlies. The characteristic timing and intensity of the ISM and Westerlies, leaves a distinct footprint on the sediment load of the River. Analysis with the last forty years data, shows an increasing trend for temperature. While for precipitation during the same period, the Spiti watershed on the west has highest monthly accumulated precipitation with long term declining trend, in contrast to the other areas where an increasing trend has been observed. Thus, to probe the hydrological variability and the seasonal attributes, governed by the Westerlies and ISM in the study area, we analyzed precipitation, temperature, snow cover area (in %), discharge, suspended sediment concentration (SSC) and suspended sediment load (SSL) for the period 2004 - 2008. To accomplish the task, we used the available data of five hydrological stations located in the study area. Inter-annual shift in peak discharge during the monsoon period is controlled by the variation in precipitation, snow melt, glacier melt and temperature. Besides seasonal variability has been observed in generation of the sediments and its delivery to the river. Our analysis indicates, dominance of the Westerlies footprints in the hydrological parameters of the Spiti region, towards western part of the study area. While, it is observed that the hydrology of the Khab towards eastern part of the study area shows dominance of ISM. Further downstream, the hydrology of Nathpa station also shows dominance of ISM. It also emerged out that the snowmelt contribution to the River flow is mostly during the initial part, at the onset of the monsoon, while for rest and major part of the summer monsoon season, the River flow is augmented by the precipitation, glacial melt and some snow melt. We observed, that the SSC increases exponentially in response to increase in temperature and correlates positively with River discharge. The average daily SSL in the summer monsoon is many times more than that in the winter monsoon. The downstream decrease in steepness of the sediment rating curve is attributed to either a change in the River-sediment dynamics or on account of the anthropogenic forcing. The top 1% of the extreme summer monsoon events (only 4 events) in our study area contribute up to 45% of SSL to the total sediment load budget. It has also been observed that the River-sediment dynamics in the upstream catchments are more vulnerable and sensitive to the extreme events in comparison to the downstream catchments. The present study for the first time gives a holistic insight in to the complex dynamics of the hydrological processes operational in the study area. The research findings would be crucial for managing the water resources of the region and the linked water and food security.

The suspended sediment load (SSL) prediction is one of the most important issues in water engineering. In this article, the adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) were used to estimate the SLL of two main tributaries of the Telar River placed in the north of Iran. The main Telar River had two main tributaries, namely, the Telar and the Kasilian. A new evolutionary algorithm, namely, the black widow optimization algorithm (BWOA), was used to enhance the precision of the ANFIS and SVM models for predicting daily SSL. The lagged rainfall, temperature, discharge, and SSL were used as the inputs to the models. The present study used a new hybrid Gamma test to determine the best input scenario. In the next step, the best input combination was determined based on the gamma value. In this research, the abilities of the ANFIS-BWOA and SVM-BWOA were benchmarked with the ANFIS-bat algorithm (BA), SVM-BA, SVM-particle swarm optimization (PSO), and ANFIS-PSO. The mean absolute error (MAE) of ANFIS-BWOA was 0.40%, 2.2%, and 2.5% lower than those of ANFIS-BA, ANFIS-PSO, and ANFIS models in the training level for Telar River. It was concluded that the ANFIS-BWOA had the highest value of R2 among other models in the Telar River. The MAE of the ANFIS-BWOA, SVM-BWOA, SVM-PSO, SVM-BA, and SVM models were 899.12 (Ton/day), 934.23 (Ton/day), 987.12 (Ton/day), 976.12, and 989.12 (Ton/day), respectively, in the testing level for the Kasilian River. An uncertainty analysis was used to investigate the effect of uncertainty of the inputs (first scenario) and the model parameters (the second scenario) on the accuracy of models. It was observed that the input uncertainty higher than the parameter uncertainty.

Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period (1980-2015). The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R2). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.

The study aims to evaluate the performance of four sediment rating curve development methods, namely (i) simple rating curve, (ii) different ratings for the dry and wet season of the year, (iii) different ratings for the rising and falling limb of the runoff hydrograph, and (iv) broken line interpolation that uses different exponents for two discharge classes at the outlet of the Venetikos River catchment, located at Western Macedonia, Northern Greece. The goal is to provide guidance on the selection of the most appropriate one for the estimation of sediment discharge (yield) at this gauging site (basin), as well as to properly assess such values. The necessary field measurements (discharge, sediment discharge, discharge-sediment discharge pairs) were conducted by the Greek Public Power Corporation. The performance of each method was evaluated by executing a statistical analysis (1965-1982), using as benchmark the observed mean monthly sediment discharge values. The broken line interpolation method performed best, not only by meeting the desired criteria of most statistical indicators used but also by being overall superior to all other methods. Thus, henceforward is to be treated as the representative rating curve development method for the specific site. Finally, an attempt was made to evaluate the estimated (and observed) sediment yield values against the ones attributed by four empirical equations, yet with relatively poor results.

Suspended sediment load (SSL) modelling is an important issue in integrated environmental and water resources management, as sediment affects water quality and aquatic habitats. Although classification and regression tree (CART) algorithms have been applied successfully to ecological and geomorphological modelling, their applicability to SSL estimation in rivers has not yet been investigated. In this study, we evaluated use of a CART model to estimate SSL based on hydro-meteorological data. We also compared the accuracy of the CART model with that of the four most commonly used models for time series modelling of SSL, i.e. adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron (MLP) neural network and two kernels of support vector machines (RBF-SVM and P-SVM). The models were calibrated using river discharge, stage, rainfall and monthly SSL data for the Kareh-Sang River gauging station in the Haraz watershed in northern Iran, where sediment transport is a considerable issue. In addition, different combinations of input data with various time lags were explored to estimate SSL. The best input combination was identified through trial and error, percent bias (PBIAS), Taylor diagrams and violin plots for each model. For evaluating the capability of the models, different statistics such as Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and percent bias (PBIAS) were used. The results showed that the CART model performed best in predicting SSL (NSE=0.77, KGE=0.8, PBIAS<±15), followed by RBF-SVM (NSE=0.68, KGE=0.72, PBIAS<±15). Thus the CART model can be a helpful tool in basins where hydro-meteorological data are readily available.

Understanding of the distribution patterns of sediment erosion, concentration and transport in river basins is critically important as sediment plays a major role in river basin hydrophysical and ecological processes. In this study, we proposed an integrated framework for the assessment of sediment dynamics, including soil erosion (SE), suspended sediment load (SSL) and suspended sediment concentration (SSC), and applied this framework to the Mekong River Basin. The Revised Universal Soil Loss Equation (RUSLE) model was adopted with a geographic information system to assess SE and was coupled with a sediment accumulation and a routing scheme to simulate SSL. This framework also analyzed Landsat imagery captured between 1987 and 2000 together with ground observations to interpolate spatio-temporal patterns of SSC. The simulated SSL results from 1987 to 2000 showed the relative root mean square error of 41% and coefficient of determination (R(2)) of 0.89. The polynomial relationship of the near infrared exoatmospheric reflectance and the band 4 wavelength (760-900nm) to the observed SSC at 9 sites demonstrated the good agreement (overall relative RMSE=5.2%, R(2)=0.87). The result found that the severe SE occurs in the upper (China and Lao PDR) and lower (western part of Vietnam) regions. The SSC in the rainy season (June-November) showed increasing and decreasing trends longitudinally in the upper (China and Lao PDR) and lower regions (Cambodia), respectively, while the longitudinal profile of SSL showed a fluctuating trend along the river in the early rainy season. Overall, the results described the unique spatio-temporal patterns of SE, SSL and SSC in the Mekong River Basin. Thus, the proposed integrated framework is useful for elucidating complex process of sediment generation and transport in the land and river systems of large river basins.