Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

Understanding hydrocarbon microseepage mechanism is important for interpreting hydrocarbon anomalies in the surface geochemical exploration. An experiment was developed to simulate the vertical microseepage of hydrocarbons with multiple lithological layers. The results revealed that the microseepage below the water table was dominated by buoyancy of microbubbles with diffusion as a secondary mechanism, however, it was dominated by diffusion with little effect by gas convection above the water table. The micro-fractures were the dominant channels for microseepage. A phenomenological model was developed to quantify the vertical microseepage in different media and provided a better tool in a field environment.

The Sabine–Neches Waterway (SNWW) is home to the largest commercial port of the United States military and of the refineries that produce 60% of the nation’s commercial jet fuel. The deposited sediments from bank erosion due to wake wash result in frequent dredging to keep the waterway operational. This study investigates vessel-generated waves and their impacts on bank erosion. Surface wave data at Golden Pass and the City of Port Arthur Park dock were measured using a 1 MHz Aquadopp Profiler. Bank properties such as soil strengths were measured and soil samples were collected. Acceptable predictive models for estimating the maximum wave heights caused by vessels sailing through the SNWW were developed and validated with recorded data. Vessel-generated waves are found to produce enough shear forces to mobilize bed sediments and cause bank erosion. The bed erosion rate increases with an increase in wave height or a decrease in water depth. Bank and bank toe erosion occurs at both monitoring locations. Bank stability and toe erosion model (BSTEM) results suggest that potential bank protection options are large woody debris and riprap at Port Arthur. However, other stronger stabilization methods are required at Golden Pass.

Sediments are dredged from waterways to maintain maritime activities and prevent floods. Exorbitant amounts of money are budgeted for the removal of dredged material (DM) and its disposal in landfills. We investigated the potential for reuse of DM as a road construction material using so-called bio-enzyme products as stabilizing agents. To improve the mechanical properties of DM, such as compressive strength, compressibility, Atterberg limits and the California bearing ratio (CBR), mixtures of DM were tested with two different amounts of a commercially available bio-enzyme product, which yielded enzymatically stabilized dredged material (ESDM). Unconfined compressive strength (UCS), compaction and Atterberg limits were measured in accordance with ASTM specifications on all samples. Data show that the addition of bio-enzymes resulted in increases in UCS but did not affect the optimum moisture content (OMC), maximum dry unit weight or Atterberg limits of the DM. A comparative field study was carried out to evaluate the CBR of the CH subgrade before and after treatments with the bio-enzyme product and with lime as a traditional stabilizing agent. The results of the field study supported the laboratory findings. Based on these data and results from the literature, models predicting the effect of bio-enzyme treatments on the value of CBR and of UCS were developed statistically. These models also underlined the importance of the clay fraction and PI values for the improvement of the engineering properties of soil using bio-enzyme additives.