YellowScan's Navigator system is designed to map underwater topography in rivers, ponds and coastal areas. Below, its lidar system scans the landscape, mapping both the land and the shallow waters with pinpoint accuracy. This is precisely what YellowScan's new Navigator system is designed to do. Built specifically for mapping underwater topography in rivers, ponds and coastal areas, the Navigator is changing the game for environmental monitoring. With precision where traditional methods struggle, it's giving researchers and conservationists a whole new way to understand our planet's changing waterways.

The growing impact of climate change on coastal areas, particularly active but fragile regions, necessitates collaboration among diverse stakeholders and disciplines to formulate effective environmental protection policies. We introduce a novel specialized corpus comprising 2,491 sentences from 410 scientific abstracts concerning coastal areas, for the Automatic Term Extraction (ATE) and Classification (ATC) tasks. Inspired by the ARDI framework, focused on the identification of Actors, Resources, Dynamics and Interactions, we automatically extract domain terms and their distinct roles in the functioning of coastal systems by leveraging monolingual and multilingual transformer models. The evaluation demonstrates consistent results, achieving an F1 score of approximately 80\% for automated term extraction and F1 of 70\% for extracting terms and their labels. These findings are promising and signify an initial step towards the development of a specialized Knowledge Base dedicated to coastal areas.

Monitoring water contaminants is of paramount importance, ensuring public health and environmental well-being. Turbidity, a key parameter, poses a significant problem, affecting water quality. Its accurate assessment is crucial for safeguarding ecosystems and human consumption, demanding meticulous attention and action. For this, our study pioneers a novel approach to monitor the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with high-resolution data from Sentinel-2 Level-2A. Traditional methods are labor-intensive while CatBoost offers an efficient solution, excelling in predictive accuracy. Leveraging atmospherically corrected Sentinel-2 data through the Google Earth Engine (GEE), our study contributes to scalable and precise Turbidity monitoring. A specific tabular dataset derived from Hong Kong contaminants monitoring stations enriches our study, providing region-specific insights. Results showcase the viability of this integrated approach, laying the foundation for adopting advanced techniques in global water quality management.

Our society relies heavily on plastic products and the amount of plastic waste is expected to increase in the future. If not properly discarded or recycled, much of it accumulates in rivers and lakes. Eventually it will flow into the oceans, where it can form aggregations of marine debris together with natural materials like driftwood and algae. A new study from Wageningen University and EPFL researchers, recently published in Cell iScience, has developed an artificial intelligence-based detector that estimates the probability of marine debris shown in satellite images. This could help to systematically remove plastic litter from the oceans with ships.

Seawater has a pH of about 8.2, although it can vary between 7.5 and 8.5 depending on local salinity, and is estimated to have declined on average by 0.1 since the industrial era. This downward trend associated with increasing CO2 levels in the atmosphere is a matter of concern because of the possible negative consequences for marine organisms, especially calcifiers (corals, shellfish …). A team of Spanish researchers conducted a study to assess the seasonal variability of pH. Entitled " pH trends and seasonal cycle in the coastal Balearic Sea reconstructed through machine learning", it was published in the journal Natureon July 28. Susana Flecha, Àlex Giménez-Romero, Joaquín Tintoré, Fiz F. Pérez, Iris E. Hendriks, Manuel A. Matías, Eva Alou-Font are the authors of this study, which aims to study the variability of the PH of the Balearic coastal area through deep learning.

Essentially, the team is using artificial intelligence and machine learning to predict future flooding. "The focus here is on hazards such as …

Coastal communities around the world are especially vulnerable to flooding, storms, hurricanes and heavy rainfall. Now, scientists are studying whether artificial intelligence can better predict the impact of the storms. More information would help areas like New Orleans, Louisiana, which is forced to fix and rebuild after severe flooding. Clint Dawson, a professor at the University of Texas Austin, is part of a team of investigators working on a project funded by the Department of Energy's Office of Advanced Scientific Computing Research. "The only reason that place still exists is because there is fairly extensive levy system that protects it," Dawson said.

It was last summer that I wrote about the Japanese computing giant'Fugaku' surpassing the American reigning champion Summit to become the fastest supercomputer in the World. Since then, Fugaku has solidified its position at the top spot -- according to the 56th edition of the TOP500 list published in Nov. 2020, its capacity has increased from 7,299,072 cores to 7,630,848 cores, posting a new world record 442 petaflops result on HPL. The most powerful supercomputer by RIKEN Center for Computational Science & Fujitsu has now been engaged in developing a real-world prediction tool. In a multinational collaborative endeavor, The International Research Institute of Disaster Science at Tohoku University, the Earthquake Research Institute at the University of Tokyo, and Fujitsu Laboratories have come together to develop an AI model that will be able to predict tsunami flooding in coastal areas in near real-time. This could be a real handy tool for disaster management teams.

There is a small number of case studies of automatic land cover classification on the coastal area. Here, I test extraction of seagrass beds, sandy area, oyster farming rafts at Mangoku-ura Lagoon, Miyagi, Japan by comparing manual tracing, simple image segmentation, and image transformation using deep learning. The result was used to extract the changes before and after the earthquake and tsunami. The output resolution was best in the image transformation method, which showed more than 69% accuracy for vegetation classification by an assessment using random points on independent test data. The distribution of oyster farming rafts was detected by the segmentation model. Assessment of the change before and after the earthquake by the manual tracing and image transformation result revealed increase of sand area and decrease of the vegetation. By the segmentation model only the decrease of the oyster farming was detected. These results demonstrate the potential to extract the spatial pattern of these elements after an earthquake and tsunami. Index Terms: Great East Japan Earthquake of 2011, Land use land cover (LULC), Zosteracea seagrass, cultured oyster, deep learning, Mangoku Bay