The fluctuation of the tide is closely related to the production and life of people in coastal areas, and the change in the tide level will have a significant impact on the safety of infrastructure, ship travel, ecological environment, and other issues. Therefore, it is of great significance to analyze, study, and forecast the change in tide level. Aiming at the complex characteristics of nonlinearity, time-varying dynamics, and uncertainty generated by celestial bodies’ movements and influenced by geographical as well as hydrometeorological factors, this paper proposes a combined model based on variational mode decomposition (VMD) and long short-term memory neural networks (LSTM). A tidal level prediction procedure is proposed by combining the harmonic analysis method with a neural network and takes the point tide data of Luchao Port from 2021 to 2022 as the applied data. First, the VMD algorithm decomposes the tidal level data into model components. Then, the LSTM model is used to predict each component. Finally, the predicted value of each component is superposed to obtain the final prediction result. Standard evaluation indexes were used to analyze the performance of the proposed model. The model’s RMSE, MAE, MAPE, and R² were 0.0385, 0.0267, 5.8327, and 99.91%, respectively, superior to other compared models (BP, SVM, and LSTM). This study can provide a reference for tidal level prediction. These results show that the VMD-LSTM model is an effective and reliable tidal level prediction tool with considerable potential in offshore engineering and maritime management.

Accurate prediction of estuarine salinity can effectively mitigate the adverse effects of saltwater intrusion and help ensure the safety of water resources in estuarine regions. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity and obtained considerable achievements. Due to the nonlinear and nonstationary features of estuarine salinity sequences, this paper proposed a multi-factor salinity prediction model using an enhanced Long Short-Term Memory (LSTM) network. To improve prediction accuracy, input variables of the model were determined through Grey Relational Analysis (GRA) combined with estuarine dynamic analysis, and hyperparameters for the LSTM model were optimized using a multi-strategy Improved Sparrow Search Algorithm (ISSA). The proposed ISSA-LSTM model was applied to predict salinity at the Cangqian and Qibao stations in the Qiantang Estuary of China, based on measured data from 2011–2012. The model performance is evaluated by mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE). The results show that compared to other models including Back Propagation neural network (BP), Gate Recurrent Unit (GRU), and LSTM model, the new model has smaller errors and higher prediction accuracy, with NSE improved by 8–32% and other metrics (MAP, MAPE, RMSE) improved by 15–67%. Meanwhile, compared with LSTM optimized with the original SSA (SSA-LSTM), MAE, MAPE, and RMSE values of the new model decreased by 13–16%, 15–16%, and 11–13%, and NSE value increased by 5–6%, indicating that the ISSA has a better hyperparameter optimization ability than the original SSA. Thus, the model provides a practical solution for the rapid and precise prediction of estuarine salinity.

Qiantang River Estuary, as one of main fresh water resources for urban zone, has been threatened by salt water intrusion. Due to the combined influence of tide and runoff, the mechanism of saltwater intrusion is extremely complex. In view of this, research on the variation characteristics of chlorinity along Qiantang River Estuary is conducted based on chlorinity data. Further, analysis on effects that tide and upstream runoff exerting on chlorinity at intake is carried out to ensure urban water security. The chlorinity and tide data was obtained synchronously from automatic real-time monitoring systems at Zhakou, Qibao, Cangqian, Yanguan and Ganpu stations with sampling interval of 5 minutes. Results show that chlorinity Yanguan and Ganpu are mainly influenced by tide with period of 12.4 h; chlorinity at other stations are affected by runoff obviously. The peak chlorinity values at all observed stations increase with increase of tide ranges. The increase of upstream runoff enables to reduce the peak chlorinity. It is feasible to extract water under average tide and average runoff.

To quantitatively reveal the research status, knowledge structure, and development trends of automated suspended sediment monitoring technologies and their applications, this study retrieved relevant literature from the Web of Science Core Collection (1976–15 April 2026). After screening, 1926 publications were analyzed using Bibliometrix, VOSviewer, and CiteSpace, covering collaboration networks, co-citation, historical direct citation, keyword co-occurrence clustering, and burst detection. The results indicate sustained growth in this field: China ranks first in publications, and the Chinese Academy of Sciences is the most productive institution. The research is markedly interdisciplinary, encompassing environmental science, earth science, oceanography, remote sensing, and water resources; Remote Sensing of Environment, Continental Shelf Research, and Journal of Geophysical Research: Oceans are important journals in the co-citation network. Co-citation analysis shows that the knowledge base comprises three pillars: ocean-color remote sensing retrieval and atmospheric correction, in-situ observation and hydrodynamic transport of suspended sediment, and sediment dynamics and environmental responses across watersheds, estuaries, and coastal zones. Historical direct-citation analysis further reveals that key findings have advanced along three trajectories: in-situ acoustic and optical monitoring, remote sensing retrieval in highly turbid waters, and riverine–wetland applications and large-scale sediment research. Over the past six years, hotspots have centered on multi-source remote sensing and water optical monitoring, machine learning and intelligent remote sensing data processing, sediment dynamics and in-situ monitoring, and river sediment transport and estuarine-coastal sediment processes. Keyword-burst analysis shows machine learning exhibiting the strongest recent burst since 2023, while research on sediment load and flux based on automated suspended sediment monitoring technologies has become active since 2024, indicating the field’s expansion from automated acquisition of suspended-sediment concentration toward multi-source fusion, intelligent retrieval, and sediment-transport discharge estimation.

The integration of distributed Photovoltaic (PV) power sources into the grid has significantly increased the proportion of harmonics and interharmonics, thereby rendering traditional electric energy measurement systems unable to precisely evaluate electrical energy. This study proposes an innovative electric energy measurement approach to enhance accuracy for systems incorporating PV dynamic characteristics, based on an Improved S-transform that incorporates an optimised Kaiser window. To overcome the limitation of the fixed timefrequency resolution in the S-transform, a time-frequency adaptive Kaiser window function is proposed. Then, to improve time-frequency concentration, a scaling factor is integrated into the Kaiser window, and the Marine predator algorithm (MPA) to optimise the scaling factor's window shape parameters, thus forming the Improved Kaiser window based S-transform (IKST) algorithm. Then apply the IKST algorithm to PV grid-connected electric energy measurement. Furthermore, to validate the effectiveness of the IKST algorithm, complex dynamic signals were employed for verification. The results demonstrated its ability to precisely separate fundamental, harmonic, and inter-harmonic components, exhibiting highprecision time-frequency characteristics. Utilising Ré nyi entropy, it was verified to possess robust performance against parameter variations and noise. Finally, leveraging the superior performance of the IKST algorithm, it analyses complex dynamic signals and PV grid connected output signals. Combined with this algorithm, it enables the measurement of fundamental waves, harmonics, interharmonics, and total electrical energy. Simulation results indicate that for dynamic signals originating from typical power electronic inverter components, the total electrical energy measurement error achieved by the IKST algorithm is merely 0.16%, representing a 73.33% improvement in accuracy compared to the S-transform. Within this model, the total electrical energy measurement error for PV grid connected systems is only 0.04%, with measurement values enhanced by 80.00%. This validates that the proposed method achieves significantly higher accuracy than the existing S-transform electric energy measurement approach.