Search Results (318)

Dissolved oxygen (DO) is a critical indicator for assessing marine ecological health and hypoxia risk. Most existing DO prediction studies rely on time-series forecasting models, which require continuous temporal observations and are often unreliable in practical marine monitoring scenarios due to sparse sampling, missing records, and heterogeneous measurement conditions. To address this limitation, this paper investigates the problem of non-temporal DO prediction, aiming to learn a direct nonlinear mapping between environmental drivers and DO concentration. To explicitly model nonlinear pairwise interaction effects between environmental variables, we propose a Factor-Interaction Neural Network (FINN), which decomposes DO estimation into main effects and structured pairwise interaction effects. This interaction-driven design enhances both representation capacity and interpretability compared with conventional multilayer perceptrons. Furthermore, we develop a physics-informed extension, termed PI-FINN, by incorporating oceanographic-consistent regularization priors that reflect key DO formation mechanisms, including temperature-related solubility behavior, depth-wise smoothness associated with stratification, and chlorophyll-driven biological oxygen production tendencies. To evaluate the physical plausibility of model predictions beyond standard accuracy metrics, we introduce a physics-consistency assessment protocol based on Physics Consistency Violation Rate (PCVR) and its robust variant, and further analyze their convergence stability under different driver-weight configurations. Extensive experiments on a real-world marine dataset demonstrate that FINN achieves competitive predictive accuracy compared with strong machine learning baselines (e.g., SVR, Random Forest, and XGBoost), while the proposed physics-informed design mainly improves the physical consistency, robustness, and interpretability of DO estimation under heterogeneous environmental regimes, although it does not necessarily guarantee superior RMSE or MAE performance compared with purely data-driven models. Specifically, FINN achieves an RMSE of 0.3130, an $R^2$ of 0.9831, and a PCVR of 0.4826 on a dataset composed of key environmental variables, including depth, temperature, salinity, and chlorophyll-a, collected under sparse and irregular sampling conditions. Ablation studies confirm the effectiveness of both factor-interaction modeling and physics-guided regularization components. Overall, the proposed framework further provides a reliable tool for sustainable environmental monitoring by enabling physically consistent dissolved oxygen prediction under sparse observational conditions. Such capability is critical for supporting sustainable water resource management, hypoxia risk assessment, and long-term ecological protection. Full article

River water quality prediction is a crucial aspect of water environment management and ecological conservation, holding significant importance for ensuring the sustainable utilization of water resources. As a key indicator for assessing river self-purification capacity and aquatic ecosystem health, the accurate prediction of dissolved oxygen (DO) is particularly vital for water quality early warning. To address the challenges that single deep learning models face in collaboratively modeling long- and short-term dependencies, and that most hybrid methods fail to adequately consider the characteristic differences in various components within a time series, this paper proposes an STL-TCN-LSTM model for predicting DO concentration in river water. The proposed model first employs seasonal-trend decomposition using Loess (STL) to decompose the original time series into three components: trend, seasonality, and residual, aiming to separate features at different time scales. Then, three parallel Temporal Convolutional Networks (TCNs) are utilized to extract temporal features from each component and reconstruct the sequence. Finally, the reconstructed results are fed into a Long Short-Term Memory (LSTM) network to further model their dynamic temporal dependencies, thereby enhancing prediction accuracy. The performance of the proposed model is validated on three river water quality datasets from different river basins with varying sampling frequencies. The experimental results on the three river datasets show that the STL-TCN-LSTM model consistently outperforms all baseline models, including LSTM, TCN, BiLSTM, GRU, CNN-LSTM, and XGBoost. Specifically, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are reduced by an average of 14.47%, 14.51%, and 14.27%, respectively, while the coefficient of determination (R²) improves by an average of 0.79%. The Wilcoxon signed-rank test confirms that all performance improvements are statistically significant (p < 0.05). These results demonstrate that the proposed model achieves higher prediction accuracy and exhibits stronger generalization capability in DO forecasting, thereby offering a reliable tool for water quality early warning and aquatic environmental management. Full article

A comprehensive understanding of the spatial dynamics and operational characteristics of fishing activities in the Arabian Sea is critical for effective marine management and regional resource conservation. Based on VIIRS/DNB nighttime light imagery from 2017 to 2022 and the YOLOv11 model, this study presents an applied observational pipeline for the spatial extraction of fishing vessel positions. Spatial statistical methods were employed to analyze the operational patterns of light-fishing fleets, and habitat niches were identified by integrating marine environmental data. The results indicate that: (1) The YOLOv11 model achieved a precision (P) of 0.966, a recall (R) of 0.954, and a mean average precision (mAP) of 0.969. Under clear-sky and thin-cloud conditions, it demonstrated superior detection accuracy compared to existing VBD (VIIRS Boat Detection) products. (2) Through Kernel Density Hotspot Analysis (KDHSA), the primary spatial distribution of the light-fishing fleet was delineated. Fishing Operation Areas (FOAs) exhibited a pronounced seasonal “clustering–diffusion–re-clustering” pattern. The Center of Effort (CoE) generally followed a counter-clockwise migration trajectory, though a clockwise shift was observed during the 2019–2020 fishing season. (3) Random Forest analysis identified dissolved oxygen at 200 m (DO200), sea surface height (SSH), and temperature at 200 m (T200) as the primary predictive environmental features associated with vessel distribution. The core spatial ranges associated with high vessel density were 9.5–14.9 mmol⋅m⁻³ for DO200, 0.24–0.36 m for SSH, and 17.3–18.0 °C for T200. Notably, the statistical contribution of subsurface factors significantly exceeded that of sea surface temperature (SST). Future research should integrate ship position data with fishery biological data to further explore the drivers of FOA variations. This study provides a scientific basis for the sustainable management and rational development of marine resources in the Northwest Indian Ocean. Full article

Reducing energy consumption while maintaining stable water quality remains a major challenge in ornamental aquaculture. This study proposes an integrated predictive and energy-aware aquaculture management framework combining Internet of Things (IoT) sensing, Long Short-Term Memory (LSTM)-based prediction, Digital Twin (DT) simulation, and Cyber-Physical System (CPS) control. Real-time sensor networks monitored dissolved oxygen (DO), ammonia (NH₃), temperature, pH, turbidity, and energy consumption in a koi pond over a 45-day deployment period. Forecasted environmental states generated by the LSTM model were validated through a physics-informed Digital Twin prior to actuator execution to improve operational reliability and control safety. Experimental results demonstrated strong agreement between the Digital Twin and observed pond dynamics, achieving R² values of 0.97 for dissolved oxygen and 0.94 for ammonia. Compared with conventional manual operation, the proposed smart predictive control mode reduced total energy consumption by 26.86%. Statistical analysis confirmed that the reduction was highly significant (p < 0.001), with average daily energy consumption decreasing from 212 ± 6.06 Wh/day under manual operation to 154.71 ± 4.52 Wh/day under smart predictive control. Full article

Water-quality degradation in semi-arid basins is strongly influenced by spatial heterogeneity and cumulative anthropogenic pressure. This study characterises spatial gradients, identifies contamination hotspots, and evaluates system behaviour in the Jipijapa River micro-basin, Ecuador, through an integrated analytical framework. A multi-year dataset (2023–2025; n = 27) from nine monitoring sites was analysed using non-parametric statistics, regulatory exceedance-based hotspot detection, the BMWP/Col index, Spearman correlations adjusted by false discovery rate, and exploratory machine-learning models (Random Forest and ε-SVR) with leave-one-out cross-validation. Results showed a significant longitudinal gradient, with dissolved oxygen decreasing from 6.1 to 2.1 mg L⁻¹ and BOD₅ increasing from 6.1 to 111.0 mg L⁻¹ downstream. Five hotspots were identified, mainly in the lower reach, while BMWP/Col values declined from 118.3 to 37.0, indicating ecological degradation. Correlation analysis revealed strong coupling between BOD₅ and dissolved oxygen (ρ = −0.916), modulated by altitude and vegetation cover. Machine-learning models showed high internal consistency, although their use was restricted to diagnostic pattern detection rather than operational prediction. Overall, the convergence of physicochemical, ecological, hotspot, and modelling evidence supports an inferred spatial resilience gradient and provides a locally adaptable framework for prioritising watershed interventions in data-limited semi-arid basins. Full article

Physicochemical transformations in wine aging are strongly influenced by storage environment and scale. While kinetic modeling has been extensively applied to bulk aging systems, bottle aging is often treated as a continuation of cellar evolution despite representing a different physicochemical regime. A reaction-kinetics framework was applied to assess whether wine aging in bulk and bottle environments can be described by a unified model or instead requires divergent quantitative descriptions. A Sangiovese red wine was aged for six months under controlled conditions in inert bulk systems (stainless steel and a non-porous composite material), a porous bulk system (raw earthenware), and glass bottles. Key physicochemical parameters, including dissolved oxygen, oxidation–reduction potential, free sulfur dioxide, anthocyanins, polymerized pigments, and colorimetric indices, were monitored through non-invasive and laboratory analysis. Exploratory multivariate analysis showed that inert systems follow overlapping compositional trajectories, indicating stable chemical evolution, whereas bottle-aged wines exhibited greater variability. Kinetic analysis revealed comparable oxygen-limited behavior and buffered oxidation–reduction evolution in inert bulk systems, whilst bottle aging displayed different oxygen and sulfur dioxide dynamics, consistent with scale effects and altered oxygen partitioning. Overall, bottle aging cannot be reliably predicted by extrapolation of bulk storage kinetics and requires boundary-condition-aware descriptors accounting for scale and environmental constraints. Full article

Predicting algal proliferation in freshwater systems is crucial for effective water quality management and ecological sustainability. This study proposes a novel data-driven framework that integrates correlation-based feature ranking with a concatenation-enhanced artificial neural network (ANN) architecture to improve algae prediction accuracy. The analysis was conducted through a systematic evaluation of parameter relationships, employing Pearson’s correlation coefficient and standardized coefficients (Beta) to determine feature importance. Based on the magnitude of these coefficients, the input variables were progressively grouped into six feature sets, enabling a comparative assessment of predictive performance. The ANN models were trained and validated using root mean squared error (RMSE), mean absolute error (MAE) and Normalized Nash–Sutcliffe Efficiency (NNSE) as evaluation metrics. The results demonstrate that the fourth feature set, including chlorophyll-a, temperature, dissolved oxygen, total dissolved solids, and ammonia (NH₃), identified through combined Pearson and Beta analysis, achieved the lowest prediction errors and superior generalization performance. These findings highlight the effectiveness of feature selection guided by correlation and standardized coefficients in enhancing ANN performance for algae prediction. The proposed framework offers valuable insights for improving the predictive modeling of algal dynamics, thereby supporting proactive water quality monitoring and the sustainable management of aquatic ecosystems. Full article

Rapid algal proliferation in human-impacted freshwater ecosystems necessitates advanced predictive tools for effective management. This study aims to capture the stochastic dynamics of algal blooms in the Fuxi River, China, using high-frequency monitoring and interpretable machine learning. A 2 h interval dataset was utilized to construct Random Forest models in Python for predicting Chlorophyll-a (Chl-a) and algal density, both measured via in situ multi-wavelength fluorescence. Model interpretability was achieved through SHAP (SHapley Additive exPlanations) analysis to identify non-linear environmental drivers and ecological thresholds. The models demonstrated high predictive accuracy. SHAP analysis revealed that dissolved oxygen (>10 mg/L) is the primary diagnostic indicator for peak Chl-a, with an optimal thermal window of 15–20 °C identified for proliferation. For algal density, chemical oxygen demand (COD_Cr > 25 mg/L) and conductivity (>1000 μS/cm) were identified as critical tipping points, showing pronounced synergistic effects between organic enrichment and nutrient levels. This study underscores that managing organic loading and monitoring specific thermal–hydrochemical windows are vital for mitigating extreme algal events, providing a robust, interpretable framework for real-time water quality early warning. Full article

Microplastics in freshwater environments provide persistent substrates for microbial colonization, forming the plastisphere. However, how trophic conditions shape plastisphere bacterial communities in drinking-water source reservoirs remains poorly understood. In this study, nine major drinking-water source reservoirs in Longyan City, Fujian Province, China, were investigated. Water quality measurements, trophic state assessment, and 16S rRNA gene amplicon sequencing were combined to characterize plastisphere bacterial communities across oligotrophic and mesotrophic reservoirs. The comprehensive trophic level index classified four reservoirs as mesotrophic and five as oligotrophic. Bacterial alpha diversity indices showed no significant trophic-dependent pattern, whereas PERMANOVA revealed significant compositional divergence between trophic groups (p < 0.01). Electrical conductivity, pH, and dissolved oxygen were the strongest correlates of community variation. Mesotrophic reservoirs were enriched in Bacillota and Bacteroidota, with biomarkers mainly affiliated with Comamonadaceae, while oligotrophic reservoirs harbored more diverse biomarkers dominated by Pseudomonadota and Cyanobacteriota. Functional prediction indicated that only aliphatic non-methane hydrocarbon degradation differed significantly between trophic groups, whereas nitrogen-cycling functions showed no significant divergence. These findings demonstrate that trophic status acts as a significant environmental filter shaping plastisphere community structure in drinking-water source reservoirs, even within a narrow oligotrophic-to-mesotrophic gradient, providing new insights for ecological risk assessment of microplastics in source-water ecosystems. Full article

Algal proliferation in subtropical drinking water reservoirs has become increasingly severe, and developing a reliable prediction for algal abundance through high-frequency in situ data is essential for early risk warning and effective management. This study analyzed the interannual variations in algal abundance in the Shanmei (SM) Reservoir, located in Quanzhou City, Fujian Province, China, based on the high-frequency data between 2020 and 2025, and forecasted algal abundance 24 h ahead via the optimized Transformer model. Results revealed that the SM reservoir exhibited seasonal variability in environmental factors, with persistently elevated pH during spring and summer, ranging from 7.12 to 9.66, and relatively high total nitrogen concentrations, ranging from 1.17 to 2.28 mg/L. Overall, algal abundance increased throughout the study period, and the annual average algal abundance in 2025 was 8.18 × 10⁶ cells/L, which was twice that in 2021. Model comparisons revealed that the optimized Transformer model exhibited the highest performance in terms of R² = 0.88 when predicting the next hour using 12 days of data. Feature importance analysis based on SHapley Additive exPlanations (SHAPs) revealed that the predictions of algal dynamics were primarily influenced by previous-hours algal abundance, permanganate index, dissolved oxygen, air temperature, wind speed, and pH. This study revealed that the optimized independent learning model with integrated multi-scale features can significantly enhance the predictive performance of algal dynamics, offering a technical basis for early warning of algal blooms and refined reservoir management. Full article

Coastal bottom dissolved oxygen (DO) depletion poses a serious threat to marine ecosystems and aquaculture, and hypoxic events in the semi-enclosed Jinhae Bay, Korea, repeatedly cause large-scale damage to fish farms. Accurate DO prediction models are therefore crucial for ecosystem management and loss mitigation. This study analyzes how different tidal input representations affect the performance of data-driven DO prediction models in a tide-dominated coastal environment. Using time-series data of oceanographic and meteorological variables from nearby observation sites, we develop an long short-term memory (LSTM)-based neural network ensemble model with four experimental configurations. These include not only water level but also tidal envelope, tidal-intensity proxy, and temporal differences in water level and DO (Δtide, ΔDO) as additional inputs. Compared with the baseline configuration, the full tide-informed input case reduced the 72 h mean root mean square error (RMSE) from 1.16 to 1.12 and increased the Pearson correlation coefficient from 0.873 to 0.883. It also improved the representation of intraday variability and prediction stability. These results show that tide-derived variables help the model more effectively capture tidal-phase-locked DO fluctuations, while temporal-difference inputs further strengthen short-term variability and sensitivity to DO changes. These results indicate that properly representing tidal forcing is essential for learning the temporal structure and variability of coastal bottom DO. Full article

Framed within data-centric artificial intelligence, this study integrates statistics, geotechnologies and AI to improve water quality prediction. The primary objective was to identify the minimum sample size required to train robust and accurate machine learning models. Based on 30 sampling points in a tropical wetland in northern Colombia, ammonium concentration was selected as the target variable, and total dissolved solids, suspended solids, phosphate, dissolved oxygen, nitrate and chemical oxygen demand were chosen as predictors. Because 30 observations are insufficient to train robust models, data augmentation was performed using ordinary kriging (OK) and empirical Bayesian kriging (EBK). From the kriging-interpolated surfaces, 1000 synthetic points (randomly and spatially distributed while preserving the estimated spatial structure) were sampled; from this expanded dataset, subsamples of varying sizes were drawn to train six algorithms: multiple linear regression (MLR), random forest (RF), k-nearest neighbours (k-NN), gradient boosting machines (GBM), multilayer perceptron (MLP) and radial basis function neural network (RBF-NN). The RF, k-NN, MLP, RBF-NN and GBM models trained on the interpolated data exhibited excellent performance: in the testing phase, they achieved adjusted coefficients of determination > 0.95 and symmetric mean absolute percentage errors (SMAPEs) < 10%, and the resulting predictive surfaces showed comparable performance under external validation. According to the criteria of stability, goodness of fit, and external validation, the optimal minimum sample size for most algorithms was 104 observations. These results represent a significant advance in mitigating data scarcity in water quality modelling. The identification of effective data augmentation methods and the determination of appropriate sample sizes, as demonstrated here, support the robust application of AI techniques in water quality prediction. The proposed strategy is transferable to other quantitative, spatially continuous environmental variables and thus contributes to the development of the emerging subdiscipline of geospatial artificial intelligence (GeoAI). Full article

To address ship-sourced water pollutant issues resulting from shipping industry growth and achieve precise supervision and effective management in coastal ports, this study develops a method for calculating and predicting the generation volume of oily sewage, domestic sewage and solid waste based on Automatic Identification System (AIS) data. First, a questionnaire survey (“Survey on Ship Water Pollutants”) is designed and implemented. Through analysis of questionnaire data, the ranges of values for the generation of oily sewage, domestic sewage, and solid waste from different ship types at China’s coastal ports are established. Additionally, onboard sampling is conducted to determine average emission factors for domestic sewage and oily sewage from typical ship types. Second, ship activities are derived from AIS data and combined with the established generation volume ranges for spatiotemporal calculation. Finally, a ConvLSTM (Convolutional Long Short-Term Memory) model is developed to predict the generation volume of water pollutant based on their spatiotemporal characteristics. Taking a major Chinese port area as a case study, the results indicate that pollutant generation volumes are significant in coastal port zones and main navigation channels, particularly between 15:00 and 16:00. chemical oxygen demand (COD), suspended solids (SS), and 5-day biochemical oxygen demand (BOD₅) levels in domestic sewage exceeded China’s national regulatory limits by 0.35 times, 2.88 times and 1.07 times, respectively, which can easily lead to a decrease in dissolved oxygen content in the water, affecting the respiration and survival of aquatic organisms. Petroleum content in oily sewage remained below the standard threshold. For pollutant generation volume prediction, the proposed ConvLSTM model achieved MAE and RMSE values of 0.0824 and 0.1433, respectively, outperforming other prediction models such as LSTM and CNN-LSTM. This research provides technical support for the prevention and control of water pollution from ships in coastal ports. The proposed AIS-driven framework and ConvLSTM prediction method are transferable and globally applicable, offering a reference for the environmental sustainability of port ecosystems, the global maritime pollution prevention, and the sustainable development of the shipping industry worldwide. Full article

Hypoxia has become increasingly frequent in the semi-enclosed Bohai Sea since the early 2000s, posing significant risks to marine ecosystems. To address the limitations of existing dissolved oxygen models—particularly their poor predictive ability and lack of interpretability—we developed a two-month lead probabilistic forecasting framework for summer hypoxia using only multi-source remote sensing and reanalysis data, supplemented by in situ observations for validation. Environmental conditions in June were used to predict hypoxia probability in August via machine learning; among the seven algorithms tested, the optimized Random Forest model achieved the best performance (F1 = 0.76 and AUC = 0.92 on the independent test set). The model successfully reproduced observed hypoxia patterns in 2019 (validated against numerical simulations) and 2022 (validated against field measurements), capturing an increase in hypoxic area from 8229 km² to 13,866 km², which is consistent with intensifying thermal stratification under climate warming. SHAP-based interpretability analysis identified reduced wind speed and enhanced thermal stratification as the dominant physical drivers, highlighting the critical role of suppressed vertical mixing in limiting bottom-water oxygen supply. This study demonstrates that a physics-informed, interpretable machine learning approach based solely on satellite and reanalysis data can deliver reliable, early, and physically consistent hypoxia forecasts, offering a scalable solution for environmental monitoring of data-limited coastal seas. Full article

Coral reefs around Hainan Island in the northern South China Sea represent a marginal reef system exposed to interacting climatic and anthropogenic stresses. This study used an optimized MaxEnt model, remote-sensing-derived coral reef occurrence data, key environmental variables, and CMIP6 climate projections to assess habitat suitability, identify key environmental thresholds associated with suitability change, and examine areas with potential refugial significance. The optimized model showed high predictive performance (mean AUC = 0.947). Bathymetry was the dominant predictor of habitat suitability, while sea surface temperature (SST) and dissolved oxygen (DO) concentration were also important predictors. Predicted suitability declined markedly when water depth exceeded 8.9 m or when multiannual mean SST exceeded 26.8 °C. Under current climate conditions, suitable habitat was limited in extent and showed strong spatial heterogeneity. Future projections indicated severe habitat contraction under SSP2-4.5 and SSP5-8.5, whereas under SSP1-1.9 suitable habitat contracted sharply by the 2050s but partially re-emerged by the 2090s. Under SSP1-1.9, parts of eastern Hainan, especially the coastal waters of southern Wenchang, Qionghai, and Wanning, may retain refugial potential. These results help clarify future spatial patterns of habitat persistence and decline, providing a scientific reference for regional conservation prioritization and adaptive management. Full article