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Reliable information of hydrological processes within a river basin is essentially required for developing an appropriate strategy for achieving sustainable development goals. The present study assesses the streamflow of a pro-glacial stream and also intends to estimate the contribution of suspended sediments, erosion rate, and the headwater contribution of the Panchachuli glacier. A field study during ablation period was carried out to measure streamflow and suspended sediment concentration (SSC). Further, HBV model was used to estimate the snowmelt. The average seasonal streamflow and SSC during the gauging period (July to October) for the basin were measured to be 7.17 m3/s, and 1.52 g/l in 2018, and 6.84 m3/s, and 1.21 g/l in 2019, respectively. Snowmelt contribution in total streamflow was 54.75% in 2018 which is reduced to 49.16% in 2019. Similarly, glacier melt contributes to 32.62% of its total runoff share in 2018 which was reduced to 28.73% in 2019. The rainfall runoff in total runoff increased to 12.62% from 2018 to 2019. Rainfall-runoff in its total runoff contribution showed an increased share of 22.13% in 2019. The streamflow, SSC, and suspended sediment load (SSL) showed a strong positive correlation for both the years. The suspended sediment yield (SSY), SSL, and erosion rate of the basin were found as high as compared to the other Himalayan basins in Himachal Pradesh, Jammu and Kashmir, and Ladakh and non-Himalayan regions that was found low when compared to other glaciers in Uttarakhand.

The estimation of sediment yield concentration is crucial for the development of stream ventures, watershed management, toxins estimation, soil disintegration, floods, and so on. In this study, we summarize various existing artificial intelligence (AI)-based suspended sediment load (SSL) estimation models to calculate the suspended sediment load, to our knowledge to date. The artificial neural network (ANN), generalized regression neural network (GRNN), neuro-fuzzy (NF), genetic algorithm (GA), gene expression programming (GEP), classification and regression tree (CART), linear regression (LR), multilinear regression (MLR), Chi-squared automatic interaction detection (CHAID), extreme learning machine (ELM), and support vector machine (SVM) are among the many AI-based models that have been successfully implemented for sediment load prediction. In this paper, we describe a few popular AI-based models that have been used for SSL prediction. ANN, SVM, and NF had overcome each other in different circumstances of prediction; and all three can be said as good predictors. Models using ANN with ELM or wavelet analysis in some ways are good predictors as their predicted values generally lie closer to the measured value. Performances of the algorithms are usually evaluated by applying various types of performance assessment methods most commonly RMSE, R2, MAE, etc. This review is required to bear some significance to the researchers and hydrologists while seeking models that have been effectively actualized inSSLestimation or in hydrology related aspects, however, mainly focused on the researches between January 2015 and November 2020.

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 R 2 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.

Precise assessment of suspended sediment load (SSL) is vital for many applications in hydrological modeling and hydraulic engineering. In this study, a smoothed long short-term memory (SM-LSTM) model was used to predict day-to-day SSL at two stations over two rivers namely Thebes station on the Mississippi River and Omaha station on the Missouri River. The model first removes the interference factors in the SSL time series by Fourier Transformation (FT) de-noising and then feeds into a long short-term memory (LSTM) network to forecast the SSL. Before de-noising, missing data in the time series is computed using the Monte Carlo multiple imputation technique. LSTM networks are a type of recurrent neural network (RNN) that incorporates memory cells, which makes them well-suited for learning temporal associations over the previous time steps. The model was built using daily observed time series of SSL in the Mississippi and Missouri rivers in the United States. The developed model was then assessed and compared to LSTM and RNN. These models were trained using 4 different time lags of the SSL time series as inputs. The SM-LSTM model with 12 lagged inputs outperformed the other models with the lowest root mean square errors (RMSE) = 32254 ton and mean absolute errors (MAE) = 19517 ton, and the highest Nash–Sutcliffe efficiency (NSE) = 0.99 for the Thebes Station while the model with 3 lagged inputs acted as the best with the lowest RMSE = 2244 ton and MAE = 1370 ton, and the highest NSE = 0.989 for the Omaha Station. The comparison of prediction accuracies showed that the SM-LSTM model can more satisfactorily predict daily SSL time series compared to LSTM and RNN.

Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations.

Estimation of suspended sediment loads (SSL) in rivers is an important issue in water resources management and planning. This study proposes a hybrid double feedforward neural network (HDFNN) model for daily SSL estimation, by combining fuzzy pattern-recognition and continuity equation into a structure of double neural networks. A comparison is performed between HDFNN, multi-layer feedforward neural network (MFNN), double parallel feedforward neural network (DPFNN) and hybrid feedforward neural network (HFNN) models. Based on a case study on the Muddy Creek in Montana of USA, it is found that the HDFNN model is strongly superior to the other three benchmarking models in terms of root mean squared error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSEC). HDFNN model demonstrates the best generalization and estimation ability due to its configuration and capability of physically dealing with different inputs. The peak value of SSL is closely estimated by the HDFNN model as well. The performances of HDFNN model in low and medium loads are satisfactory when investigated by partitioning analysis. Thus, the HDFNN is appropriate for modeling the sediment transport process with nonlinear, fuzzy and time-varying characteristics. It explores a practical alternative for use and can be recommended as an efficient estimation model for SSL.

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation ( R ), Nash-Sutcliffe efficiency coefficient ( CE ), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.

The majority of sediment transported from rivers to the global oceans is moved in suspension as fine particles. Thereby, the transported sediment shapes the physical environment regarding erosive and accumulative processes. Temporal variations in sediment supply and transport lead to unquantified uncertainties in annual load estimates, requiring high‐resolution data sets and a sound understanding of site‐specific catchment characteristics. We investigate the temporal variability of suspended sediment transport in four catchments in Germany with highly different discharge regimes and catchment sizes (<1,000 km2 to >100,000 km2). The data set consists of high‐resolution 15‐min turbidity measurements with daily discharge and frequent manual sampling. Utilizing a bootstrap approach based on the 15‐min time series, we assessed the impact of the sampling interval on annual load estimates for less frequent data sets. We use the sediment load exceedance time (Ts80%) as a measure of variability and relate it to uncertainties in annual load estimates. Since low‐frequency data sets rely on sediment rating curves, we performed a sensitivity analysis of the rating parameters a, b, and ε. Our results indicate a negative exponential relationship between Ts80% and uncertainties in annual load estimates. Based on the Ts80%, we can derive the shortest sampling frequency necessary to obtain annual load estimates with an error of <20% over varying discharge regimes. Additionally, Ts80% is linked to rating exponent b, with low b‐values indicating high Ts80%‐values and lower variability, and high b‐values indicating higher variability. Plain Language Summary On their way to the global oceans, rivers transport sediment. The transported sediment can be divided into two main fractions: fine particles and coarse sediment. Fine particles are distributed over the entire water column and represent most of the mass (sediment load) that is transported by rivers. The sediment load for rivers is not constant and varies significantly over time. Thereby, it is challenging to determine (annual) sediment loads with high accuracy. Through the combination of specialized sensors, which measure continuously, and manual water samples it is possible to reduce uncertainties in the calculation of sediment loads. First, we investigate how much data is necessary to calculate annual sediment loads with as little uncertainty as possible. Second, we investigate how much time is necessary to transport the majority (80%) of the annual sediment load. Third, we investigate how interpolation and extrapolation of small/infrequent data sets affects uncertainties in the calculation of annual sediment loads. Our results help planners and users of sediment transport data to refine (existing) sampling schemes and interpret low‐resolution data sets. Key Points Negative exponential relationship between sediment load exceedance time and uncertainties in annual load estimates Optimal sampling frequency can be derived from uncertainty‐sediment load exceedance time function Sediment rating exponent b is directly linked to sediment load exceedance time

Effective sediment monitoring is crucial for managing dynamic river environments where suspended sediment transport varies over time. However, manual sampling and turbidity sensor‐based methods provide limited spatial coverage and can be labor‐intensive. Remote sensing offers non‐contact spatial measurements but generally has low temporal resolution. To overcome these challenges, we propose closed‐circuit television‐hyperspectral imaging for suspended sediment transport (CCTV‐HISST). This framework consists of a hyperspectral CCTV system integrated with a machine learning framework and enables continuous, high‐frequency monitoring of suspended sediment concentration (SSC) during the daytime, at sunset, and overnight. Combining hyperspectral imaging with low‐light adaptability, the system can detect subtle spectral variations in sediments under natural and artificial lighting. We conducted 15 experiments using three sediment types (high‐visibility silt, low‐visibility sand, and their mixture) under controlled shallow‐water conditions in an outdoor flume. Experiments were categorized by light source: sunlight for daytime, combined sunlight and halogen lighting at sunset, and halogen lighting at night. This proof‐of‐concept study suggests that the proposed machine learning framework, light classification and adaptive regression, achieved 99% accuracy in light classification and strong agreement with field SSC measurements, even in untrained cases. Validation using field spectrometry and laser diffraction sensor data further confirmed the reliability of the proposed system. This study highlights the potential of CCTV‐HISST as a scalable, noncontact alternative for real‐time monitoring by adaptively detecting suspended sediments and quantifying their concentration across a range of light conditions. Future studies can extend its applicability to natural rivers by addressing limitations related to water depth and SSC variability.

The study of sediment‐riverflow interactions during discrete hydrological events is vital for enhancing our understanding of the hydrological cycle. Hysteresis analysis, relying on high‐resolution, continuous monitoring of suspended sediment concentration (SSC) and discharge (Q) data, is an effective tool for investigating complex hydrological events. It captures differing sediment dynamic at the same discharge level, which results from the asynchrony between the hydrograph and sediment graph during different phases of the event. However, there has been no comprehensive review systematically addressing the utility and significance of hysteresis analysis in soil and water management. This review synthesizes findings from over 500 global studies, providing a detailed examination of current research. We trace the development and application of hysteresis analysis in hydrology, illustrating its role in classifying and characterizing events, as well as uncovering sediment sources and transport mechanisms. Furthermore, hysteresis analysis has proven effective in identifying critical hydrological events, offering valuable insights for targeted watershed management. Our spatiotemporal analysis of global hysteresis research shows that over 70% of studies are located in semi‐arid and Mediterranean climate zones, with an increasing focus on alpine and tropical regions due to climate change. This review also highlights critical limitations, including the scarcity of high‐resolution data, inconsistent use of quantitative indices, and limited integration of hysteresis patterns into predictive hydrological approaches. Future research should focus on developing region‐specific hydrological models that incorporate hysteresis dynamics, along with standardizing methodologies to apply hysteresis analysis across diverse climatic and geomorphic settings. Key Points We review hysteresis methods and identify global hotspots for analyzing suspended sediment dynamic during discrete hydrological events Combining qualitative and quantitative hysteresis methods effectively classify events and reveal sediment sources and transport processes Hysteresis methods face challenges in accuracy and applicability under the growing complexity of future extreme events