Daily Suspended Sediment Load Research Articles

Predicting Daily Suspended Sediment Load in Rivers Using Hybrid and Deep Learning Models-Case Study: Kharestan Watershed

Predicting Daily Suspended Sediment Load in Rivers Using Hybrid and Deep Learning Models-Case Study: Kharestan Watershed

Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

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.

Modeling of daily suspended sediment load by trivariate probabilistic model (case study, Allah River Basin, Iran)

Modeling of daily suspended sediment load by trivariate probabilistic model (case study, Allah River Basin, Iran)

A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction

A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction

Mode decomposition based large margin distribution machines for sediment load prediction

Precise suspended sediment load (SSL) prediction is essential for irrigation, hydropower and river management practices. But due to some external factors such as high altitude, heavy monsoon and tropical climate conditions, the data we collect may contain noisy samples. Hence, it becomes a challenging task to accurately predict the SSL in rivers. Therefore, to diminish the influence of noise, the empirical mode decomposition (EMD) -based techniques can be adopted. Moreover, the large margin distribution machine-based regression (LDMR) can deal efficiently with noisy datasets as it simultaneously minimizes the insensitive loss as well as quadratic loss. The significance of this work lies in its contribution to improving the accuracy of suspended sediment load (SSL) prediction in rivers, which has practical implications for various applications such as irrigation, hydropower, and river management. We recognize the challenges posed by external factors, which introduce noise into the collected data, making accurate prediction of SSL difficult. Overall, the significance of this work lies in its novel integration of EMD-based techniques and LDMR-based models, which address the challenges posed by noisy and non-stationary sediment load data. The findings contribute to the field of SSL prediction, offering practical solutions for managing and utilizing river resources more effectively in various domains. The main advantage of the least squares LDMR (LS-LDMR) approach is that it solves a system of linear equations rather than solving a large QPPs unlike SVR, TSVR, LDMR, which makes it computationally efficient. It is well known that the sediment load data is complex and non-stationary in nature. Therefore, for daily SSL prediction, the LDMR and least squares LDMR (LSLDMR) models are embedded with two different decomposition techniques, EMD and ensemble EMD (EEMD), to handle the nonlinear and non-stationary characteristics of sediment load data on the forecasted outputs and to increase prediction ability. The results of the proposed EMD-LDMR, EEMD-LDMR, EMD-LSLDMR and EEMD-LSLDMR are compared with the conventional support vector regression (SVR), twin SVR (TSVR), LDMR and LSLDMR. The performance of the evaluated using the best-fit model using the mean absolute error (MAE), root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), Willmott’s index (WI), correlation coefficient (CC) and R2. Better or comparable results specify the efficiency of the suggested models. For graphical visualization, scatterplots, prediction error plots and violin plots are shown. Numerical results show that these hybrid models show excellent prediction performance. The overall analysis of the results suggests using EEMD-LSLDMR for SSL estimation.

Satellite-based estimation of daily suspended sediment load using hybrid intelligent models

ABSTRACT This study uses a combination of support vector regression models, particle swarm optimization, and grey wolf optimization algorithms to predict suspended sediment load. For this purpose, The Satellite Precipitation of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Global Land Data Assimilation System (GLDAS) soil moisture products are utilized as the predictors. The prediction models are evaluated based on various visual and quantitative indicators. The Taylor and radar diagrams confirm that the support vector regression-particle swarm optimization best agrees with the observed values. Moreover, the obtained quantitative indices show that the support vector regression-particle swarm optimization model offers better performance than other models used in the present study. The values of the best indices are: Pearson correlation coefficient of 0.997, relative root mean square error of 13.17, percentage bias of 4.05, and Nash-Sutcliffe efficiency of 0.995.

Scale-specific variation in daily suspended sediment load in karst catchments

Temporal variation in sediment load is generally scale dependent and affected by many factors operating at different timescales. Due to the complexity of sediment transportation mechanisms and the nonlinear behavior of hydrological processes, identifying multi-timescale effects of variables on daily suspended sediment load (SSL) is challenging. Using multivariate empirical mode decomposition (MEMD), the aims of this study were to investigate scale-dependent relationships between daily SSL and associated variables and to predict daily SSL based on these relationships. Data on daily SSL in four typical karst catchments of southwest China and five potential variables affecting daily SSL (i.e., runoff, precipitation, air temperature, potential evapotranspiration, and Normalized Differential Vegetation Index) were collected during 2009–2012. The results indicated that MEMD decomposed temporal patterns of daily SSL and five potential variables affecting daily SSL into eight or nine intrinsic mode functions (IMFs) and a residue. Short timescale oscillations of IMF1 to IMF4 contributed more than 72 % of the total variance in daily SSL in the four catchments, indicating that temporal variation in SSL was dominated by short intervals of 4–23 days. Runoff was the dominant explanatory variable for the overall daily suspended sediment load prediction at the observation scale. The predictions of daily SSL based on the IMFs and their residuals greatly outperformed those based on the original time series data. This result implies that a single scale (e.g., observation scale) is not sufficient to capture the complex relationships that exist between daily SSL and associated variables at all timescales. The results demonstrate the superiority of MEMD in characterizing scale-specific relationships between daily SSL and associated variables and improving daily SSL predictions. Given the complexity, nonlinearity, and nonstationarity inherent in many hydrological and sediment transport processes, MEMD is recommended for identifying scale-specific variation in daily SSL in future studies.

Modelling of suspended sediment load by Bayesian optimized machine learning methods with seasonal adjustment

Suspended sediment load (SSL) is essential to river and dam engineering. Due to the complexity and stochastic nature of sedimentation, SSL prediction is a challenging task and conventional methods often fail to generate accurate results. Aiming to provide an improved estimation, this paper contributes to a new forecasting framework by integrating the seasonal adjustment (SA) and Bayesian optimization (BOP) into a machine learning (ML) model (denoted as BMS). The SA is used for de-seasonalisation and trend extraction; the BOP is to optimize the ML architecture. The BMS is evaluated using the daily SSL records from the Yangtze River. Its performance is appraised by statistical criteria of Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE). With the de-noising and hyper-parameter tuning modules, the BMS effectively heightens the accuracy of the standard ML models. The most significant improvement occurs in the Boosting model, with its augments in NSE and CC by 4.3% and 1.6%, and reductions in RMSE and MAE by 24.9% and 24.2%. The BMS gains by comparison even under flood conditions, where it remarkably reduces the errors of the constituent models by up to 47.9% for RMSE and 48.3% for MAE.

Optimizing Input variables for the Artificial Neural Network Model Using Genetic Algorithm

ANN models are known to give satisfactory results when it comes to simulation and ultimate prediction of events irrespective of the process as far as there are sufficient historical data which the machine learning technique can simulate. However, the process can be modified such that the ANN model can even give better results than it is giving presently; Genetic Algorithm Technique in WinGAmma software was utilized in analysing the dataset in order to select the most relevant combination of input variables from the dataset which will produce better performance of the ANN model and the output was compared with the normal ANN model which utilized all the input variables in the dataset and the GA-ANN model outperformed the ANN model with all input variables, which goes on the show the GA technique is very reliable and a welcome technique in selecting input variables for the optimal performance of the ANN model in simulation and prediction of events. The event simulated in this work is the Daily suspended sediment load with several input variables ranging from present and antecedent parameters of Discharge, Sediment Discharge and Suspended Sediment load. Although the result of the ANN model with all 7 input variables was satisfactory, but the GA-ANN model gave a much better result with lesser but more relevant input variables as selected by the GA technique.

Predicting Daily Suspended Sediment Load Using Machine Learning and NARX Hydro-Climatic Inputs in Semi-Arid Environment

Sediment transport in basins disturbs the ecological systems of the water bodies and leads to reservoir siltation. Its evaluation is crucial for managing water resources. The practical application of the process-based model can confront some limitations noticed in the lower accuracy during the validation process due to the lack of reliable physical datasets. In this study, we attempt to apply machine-learning-based modeling (ML) to predict the suspended sediment load, using hydro-climatic data as input variables in the semi-arid Bouregreg basin, Morocco. To that end, data for the years 2016 to 2020 were used for the training process, and the validation was performed with 2021 data. The results showed that most ML models have good accuracy, with a Nash–Schiff efficiency (NSE) ranging from 0.47 to 0.80 during the validation phase, which indicates satisfactory performances in predicting the SSL. Furthermore, the models were ranked against their generalization ability (GA), which revealed that the developed models are good to excellent in terms of GA. Overall, the present study provides new insight into predicting the SSL in a semi-arid environment, such as the Bouregreg basin.

Daily suspended sediment concentrations and load variability in karst watersheds

A better understanding of sediment dynamics and variability may provide helpful insights for improving the efficiency of community-based monitoring strategies, enhancing sediment yield estimation, and optimizing soil conservation practices. However, this issue is rarely studied in karst areas, which are experiencing severe soil erosion, and sediment concentrations and load are highly variable. The objectives of this study were to characterize the daily suspended sediment concentrations (SSC) and sediment load (SL) variability, and to test whether the watershed scale and lithology exerted substantial influence on this variability in 40 karst watersheds (420–3.4 × 105 km2) in southwest China. Several indicators of variability were used for daily SSC and SL including standard deviation, coefficient of deviation, inhomogeneity coefficient, the percentage of annual SL exported in 2% of the time (Ms2%), and the percentage of time necessary to transport 50% of the annual SL (Ts50%). Results indicated that the daily SSC and SL had high temporal variability, and the SL demonstrated stronger variability than the SSC. On average, half of the annual sediment load was discharged in<2.3% of the time in the period 2009–2012, implying that a few erosive events dominated the annual sediment load. The temporal variability of daily SSC and SL were scale dependent, and a larger watershed generally had a lower temporal variability. The Ms2% and Ts50% were relatively stable when the area of the watershed was greater than 0.15 million km2. However, no significant relationship was detected between karst coverage and Ms2% or Ts50% since karst coverage might not completely represent the characteristics of the 3-D hydrogeological structure in karst regions. The results from this study can help improve river flow management and control soil loss in karst watersheds.

Scale-Specific Variation in Daily Suspended Sediment Load in Karst Catchments

Scale-Specific Variation in Daily Suspended Sediment Load in Karst Catchments

SOME QUESTIONS OF ESTIMATING SUSPENDED SEDIMENT IN WATERCOURSES OF NORTH-WEST RUSSIA

In the practice of water management for economic purposes, calculations of suspended sediment for various time scales – from per-second to annual, are important. The estimation of annual suspended sediment transport, as well as of the statistical parameters of turbidity and the reliability of the estimates, is extremely difficult because of irregular and insufficient light conditions in different phases of the water regime. At the same time, owing to the knowledge of the processes that shape the quality of natural waters and a sufficient toolkit of calculation methods, the main indicators of water quality with can be reproduced with acceptable accuracy. The capabilities of statistical analysis of water quality indicators can be expanded by an integrated approach to estimating the indicators by means of well-proven deterministic and stochastic calculation algorithms with arguments observed regularly and for a long time. In this case, a composite method can be used to estimate the parameters of the suspended sediment distribution or water turbidity, which permits finding the parameters of the distribution curve of the function from the parameters of the distribution curve of its arguments. The paper presents a deterministic-stochastic modeling system “weather-runoff-suspended sediments”, based on a stochastic weather model, a model of runoff formation in a catchment area, and a model of annual suspended sediment transport. The system can estimate the distribution parameters of daily suspended sediment load and turbidity values in the situation of observed data shortage or changes in runoff formation in the catchment resulting from natural causes or economic activities.

Deciphering the role of meteorological parameters controlling the sediment load and water discharge in the Sutlej basin, Western Himalaya

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.

Copula-Based Infilling Methods for Daily Suspended Sediment Loads

Less-frequent and inadequate sampling of sediment data has negatively impacted the long and continuous records required for the design and operation of hydraulic facilities. This data-scarcity problem is often found in most river basins of Taiwan. This study aims to propose a parsimonious probabilistic model based on copulas to infill daily suspended sediment loads using streamflow discharge. A copula-based bivariate distribution model of sediment and discharge of the paired recorded data is constructed first. The conditional distribution of sediment load given observed discharge is used to provide probabilistic estimation of sediment loads. In addition, four different methods based on the derived conditional distribution of sediment load are used to give single-value estimations. The obtained outcomes of these methods associated with the results of the traditional sediment rating curve are compared with recorded data and evaluated in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and modified Nash-Sutcliffe efficiency (MNSE). The proposed approach is applied to the Janshou station located in eastern Taiwan with recorded daily data for the period of 1960–2019. The results indicate that the infilled sediments by the sediment rating curve exhibit better performance in RMSE and NSE, while the copula-based methods outperform in MAPE and MNSE. Additionally, the infilled sediments by the copula-based methods preserve scattered characteristics of observed sediment-discharge relationships and exhibit similar frequency distributions to that of recorded sediment data.

Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models.

Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method

Predicting suspended sediment load (SSL) in water resource management requires efficient and reliable predicted models. This study considers the support vector regression (SVR) method to predict daily suspended sediment load. Since the SVR has unknown parameters, the observer-teacher-learner-based Optimization (OTLBO) method is integrated with the SVR model to provide a novel hybrid predictive model. The SVR combined with the genetic algorithm (SVR-GA) is used as an alternative model. To explore the performance and application of the proposed models, five input combinations of rainfall and discharge data of Cham Siah River catchment are provided. The predictive models are assessed using various numerical and visual indicators. The results indicate that the SVR-OTLBO model offers a higher prediction performance than other models employed in the current study. Specifically, SVR-OTLBO model offers highest Pearson correlation coefficient (R = 0.9768), Willmott’s Index (WI = 0.9812), ratio of performance to IQ (RPIQ = 0.9201), and modified index of agreement (md = 0.7411) and the lowest relative root mean square error (RRMSE = 0.5371) in comparison with SVR-GA (R = 0.9704, WI = 0.9794, RPIQ = 0.8521, and md = 0.7323, 0.5617) and SVR (R = 0.9501, WI = 0.9734, RPIQ = 0.3229, md = 0.4338, and RRMSE = 1.0829) models, respectively.

Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir

Suspended sediment load (SSL) flowing into a reservoir contributes to the overall safety of dam. Owing to the complexity and stochastic nature of sedimentation, accurate prediction of reservoir SSL inflow is still challenging. Moreover, research and application of machine learning (ML) techniques for reservoir sedimentation are still deficient. A comprehensive evaluation of six ML models for a reservoir SSL inflow prediction was performed in this study. ML techniques including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), radial basis function neural network (RBFNN), support vector machine (SVM), genetic programming (GP), and deep learning (DL) were applied to develop predictive models of daily SSL inflow at Sangju Weir, South Korea. Significant input vectors for each model were selected with streamflow, water temperature, water stage, reservoir outflow for different time lags. Model performances were evaluated using various statistical indices including the coefficient of determination (R2), mean absolute error (MAE), percentage of bias (PBIAS), Willmott index (WI), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and Pearson correlation coefficient (PCC). The best input combinations were found to be unique for each ML model, but all six models performed reasonably well for SSL inflow predictions. ANN model outperformed other models with R2 = 0.821, MAE = 4.244 tons/day, PBIAS = 0.055, WI = 0.891, NSE = 0.991, RMSE = 11.692 tons/day, PCC = 0.826. The models were ranked based on their SSL prediction capabilities as ANN > ANFIS > DL > RBFNN > SVM > GP from best to worst. The findings are expected to be useful for future dam safety and risk assessment, and for achieving sustainability of reservoir operation through comprehensive sediment management.

Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: The case study of the Mississippi River, U.S.

Monitoring sediment transport is essential for managing and maintaining rivers. Estimation of the sediment load in rivers is fundamental for the study of sediment movement, erosion, and flood control. In the current study, three machine learning models—multi-layer perceptron (MLP), multi-layer perceptron-stochastic gradient descent (MLP-SGD), and gradient boosted tree (GBT)—were utilized to estimate the suspended sediment load (SSL) at the St. Louis (SL) and Chester (CH) stations on the Mississippi River, U.S. Four evaluation criteria including the Correlation Coefficient (CC), Nash Sutcliffe Efficiency (NSE), Scatter Index (SI), and Willmott's Index (WI) were utilized to evaluate the performance of the used models. A sensitivity analysis of the models to the input variables revealed that the current day discharge variable had the most effect on the SSL at both stations, but in the absence of current-day discharge data (Qt), a combination of input parameters including SSLt−3,SSLt−2,SSLt−1,Qt−3,Qt−2,Qt−1 can be used to estimate the SSL. The comparative outcomes indicated the high accuracy of MLP-SGD-5 model with a CC of 0.983, SI of 0.254, WI of 0.991, and NSE of 0.967 at station CH and the MLP-SGD-6 model with a CC of 0.933, SI of 0.576, WI of 0.961, and NSE of 0.867, respectively, at station SL. The results of MLP models were improved by SGD optimization. Therefore, the MLP-SGD method is recommended as the most accurate model for SSL estimation.

Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction.

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.