This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to as numerical long short-term memory (N-LSTM), is introduced. The N-LSTM takes a combined wave height representation, which is formed of a current wave height measurement and a subsequent Simulating Waves Nearshore (SWAN) numerical prediction, as the input and generates the corrected numerical prediction as the output. The correction is achieved by two modules in cascade, i.e., the LSTM module and the Gaussian approximation module. The LSTM module characterizes the correlation between measurement and numerical prediction. The Gaussian approximation module models the conditional probabilistic distribution of the wave height given the learned LSTM. The corrected numerical prediction is obtained by sampling the conditional probabilistic distribution and the corrected numerical prediction series is obtained by iterating the N-LSTM. Experimental results validate that our N-LSTM effectively lifts the accuracy of wave height numerical prediction from SWAN for the Bohai Sea and Xiaomaidao. Furthermore, compared with the state-of-the-art machine learning based prediction methods (e.g., residual learning), the N-LSTM achieves better prediction accuracy by 10% to 20% for the prediction time varying from 3 to 72 h.

We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based oil spill detection models rely heavily on big training data. However, big amounts of oil spill observation data are difficult to access in practice. To address this limitation, we developed a multiscale conditional adversarial network (MCAN) consisting of a series of adversarial networks at multiple scales. The adversarial network at each scale consists of a generator and a discriminator. The generator aims at producing an oil spill detection map as authentically as possible. The discriminator tries its best to distinguish the generated detection map from the reference data. The training procedure of MCAN commences at the coarsest scale and operates in a coarse-to-fine fashion. The multiscale architecture comprehensively captures both global and local oil spill characteristics, and the adversarial training enhances the model’s representational power via the generated data. These properties empower the MCAN with the capability of learning with small oil spill observation data. Empirical evaluations validate that our MCAN trained with four oil spill observation images accurately detects oil spills in new images.

We develop a memory graph convolutional network (MGCN) framework for sea surface temperature (SST) prediction. The MGCN consists of two memory layers: one graph layer and one output layer. The memory layer captures SST temporal changes via temporal convolution units and gate linear units. The graph layer encodes SST spatial changes in terms of characteristics derived from graph Laplacian. The output layer encapsulates information from the previous layers and produces SST prediction results. The MGCN characterizes both the temporal and spatial changes, rendering a comprehensive SST prediction strategy. We use daily mean SST data for two areas near the Bohai Sea and the East China Sea for experimental evaluations and validate that the MGCN performs better than other traditional machine learning methods for nearshore SST prediction. In addition, we test the MGCN on weekly and monthly mean SST datasets and validate that the MGCN is robust and suitable for SST prediction.

Timely and accurate representation of sea surface dynamic fields is crucial for oil spill drift prediction. Numerically forecasted sea surface dynamic fields are available in a timely manner, but their accuracy is limited. Conversely, reanalysis sea surface dynamic fields offer superior accuracy but suffer from time delays. To enhance the performance of oil spill drift prediction, we propose a deep learning-based approach to correcting numerically forecasted sea surface dynamic fields, aligning them more closely with reanalysis sea surface dynamic fields. Our approach introduces an adversarial temporal convolutional network (ATCN) framework, consisting of a temporal convolutional network (TCN)-based corrector and a discriminator. The TCN can characterize sea surface dynamic field sequences both spatially and temporally. In this scenario, the corrector processes the numerically forecasted sea surface dynamic fields and outputs corrected sea surface dynamic fields that approximate the reanalysis sea surface dynamic fields. Adversarial training with the discriminator further refines the corrector. This approach enhances timely oil spill drift prediction using the corrected sea surface dynamic fields. We also provide a dataset of oil spill drifts from the Symphony and Sanchi accidents, including related sea surface dynamic field data and oil spill remote sensing data, establishing a baseline for evaluating oil spill drift prediction. Experiments on this dataset validate the ATCN framework’s effectiveness in enhancing oil spill drift prediction.

This article presents a new method for structure assessment based on weigh-in-motion data and simulation analysis. The focus is particularly on the simply supported hollow slab bridges and extra-heavy truck (gross truck weight ≥ 80 t) from the right tail of weigh-in-motion data. The dynamic models of truck types are refined, and the coupling relationship between truck and multi-girder bridge are determined. A self-developed bridge–truck analysis system is established by separate iteration and is used in the simulation analysis of extra-heavy truck load cases. The truck load effect is first analyzed by a comparison with code values. The load rating index is introduced for the structure safety assessment; meanwhile, the key parameters, for example, the dynamic amplification factor, are optimized. The results show that the maximum load effect level of extra-heavy truck is obviously higher than the design vehicle load effect, and the average level is approximate to or a little higher than the latter. The displacements due to extra-heavy truck are seriously transfinite, while the structural stiffness can meet the requirement of serviceability. Both the proposed method and Chinese code method are used to assess the structure safety of hollow slab bridges. The maximum value of load rating index ranges from 0.69 to 0.78 and the average value is 0.53–0.67. The hollow slab bridges have not yet reached the ultimate limit state and have a safety redundancy under the extra-heavy truck load. The loading indices of MOCAT are close to the average values of load index of this study, but lower than those of the maximum values. Some extreme loading conditions may be thoughtless in the proposed safety assessment method by MOCAT.