Quantification of suspended load sediment is crucial for maintaining the ecosystem and quality of water/river bodies that serve as the habitat for many living organisms. Because the influencing factors are nonlinearly related to the suspended load sediment, it is a challenge to apply linear statistical models to predict accurately. To address such a problem, this study applied artificial intelligence (AI) methods to simulate and predict suspended load sediment. The AI methods are robust and can handle adequately issues related to nonlinearity in modelling. In the present study, four AI methods were developed to predict suspended sediment load (SSL) distribution. The methods include a backpropagation neural network, group method of data handling, least squares support vector machine, and generalised regression neural network (GRNN). In developing the respective models, drainage areas, river slopes, and length of rivers served as predictor variables while SSL was the response variable. The models were evaluated using the metrics of root mean square error (RMSE), percentage RMSE, uncertainty at 95%, RMSE observations standard deviation ratio, and Legates and McCabe index. According to
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