Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

The stability number of a breakwater can determine the armor unit’s weight, which is an important parameter in the breakwater design process. In this paper, a novel and simple machine learning approach is proposed to evaluate the stability of rubble-mound breakwaters by using Extreme Learning Machine (ELM) models. The data-driven stability assessment models were built based on a small size of training samples with a simple establishment procedure. By comparing them with other approaches, the simulation results showed that the proposed models had good assessment performances. The least user intervention and the good generalization ability could be seen as the advantages of using the stability assessment models.

Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures are proposed: a supervised support vector machine (SVM) model and an unsupervised model combining a Kohonen neural network with an SVM model (KNN-SVM). It was found that the supervised SVM model predicted the damage level of the validation samples with high accuracy, and the unsupervised data-mining KNN-SVM model agreed well with the empirical evaluation result. It is shown that both machine learning approaches could become effective tools to evaluate the damage level of spur dikes and other river training structures.

Mercury (Hg) and methylmercury (MeHg) are highly neurotoxic substances, and reservoirs play a critical role in Hg storage while providing geochemical environments conducive to MeHg production. However, the mechanisms of Hg transport and methylation during reservoir regulation remain poorly understood. This study investigates the spatial distribution of sediment Hg and MeHg in both horizontal and vertical profiles within the Nuozhadu Reservoir, located in the upper Mekong River. Using the diffusive gradients in thin films (DGT) technique, we observed that sediment Hg and MeHg concentrations initially increased and then slightly decreased from the tail to the dam front of the reservoir. This trend is primarily attributed to the transport of fine sediment particles influenced by reservoir interception. MeHg production was observed during sediment transport, but hydraulic conditions driving sediment accumulation emerged as the dominant source of sediment MeHg. Furthermore, vertical fluctuations in DGT-Hg and DGT-MeHg values were noted, likely caused by periodic reservoir operations. Our findings indicate that sediments release Hg into the overlying water, establishing sediments as a significant source of Hg pollution in reservoirs. This study enhances our understanding of Hg transport and methylation processes under the influence of reservoir regulation.

Sediment-carrying capacity prediction is an important part in the river sediments movement simulation. In this paper, a novel machine learning approach was proposed to predict the sediment-carrying capacity by using the Extreme Learning Machine (ELM). The evaluation indexes and the average errors of these prediction models were analysed in detail. The result shows that the ELM model has a good generalization performance for the sediment-carrying capacity prediction, and the ELM approach could be an effective tool for the sediment movement simulation.