DEVELOPMENT OF 3D MARINE CADASTRE DATA MODEL BASED ON LAND ADMINISTRATION DOMAIN MODEL

A new version of the Land Administration Domain Model (LADM) has been discussed and is under further development in ISO/TC 211 on Geographic Information. One of the extending parts is where the model can accommodate complex and advanced marine properties and cadastral objects. Currently, the fundamentals part of this new version (LADM Edition II) has been examined by the committee, and a few elements need to be considered, especially for marine space georegulation. Based on the possibility of embedding LADM with marine cadastre as agreed by several researchers, the concept of marine cadastre data model within land administration context has been anticipated in many countries (e.g., Canada, Greece, Turkey, Australia, and Malaysia). Part of the research focused on constructing and developing the appropriate data models to manage marine spaces and resources most effectively. Several studies have attempted to establish a conceptual model for marine cadastre in Malaysia. However, there is still no acceptable marine data model. Thus, this paper proposed a marine data model for Malaysia based on the international standard, LADM. The approach, by definition, can be applied to the marine environment in terms of controlling and modelling a variety of rights, responsibilities, and restrictions. The Unified Modelling Language (UML) application was utilized to construct the conceptual and technical models via Enterprise Architect as part of the validation process. The data model was constructed within the marine's concept in Malaysia to meet international standards. The features of the data model were also discussed in the FIG workshop (9th LADM International Workshop 2021). The experiment on the data model also includes 3D visualization and simple query.

Perspectives on Marine Data Science as a Blueprint for Emerging Data Science Disciplines

Earth System Sciences have been generating increasingly larger amounts of heterogeneous data in recent years. We identify the need to combine Earth System Sciences with Data Sciences, and give our perspective on how this could be accomplished within the sub-field of Marine Sciences. Marine data hold abundant information and insights that Data Science techniques can reveal. There is high demand and potential to combine skills and knowledge from Marine and Data Sciences to best take advantage of the vast amount of marine data. This can be accomplished by establishing Marine Data Science as a new research discipline. Marine Data Science is an interface science that applies Data Science tools to extract information, knowledge, and insights from the exponentially increasing body of marine data. Marine Data Scientists need to be trained Data Scientists with a broad basic understanding of Marine Sciences and expertise in knowledge transfer. Marine Data Science doctoral researchers need targeted training for these specific skills, a crucial component of which is co-supervision from both parental sciences. They also might face challenges of scientific recognition and lack of an established academic career path. In this paper, we, Marine and Data Scientists at different stages of their academic career, present perspectives to define Marine Data Science as a distinct discipline. We draw on experiences of a Doctoral Research School, MarDATA, dedicated to training a cohort of early career Marine Data Scientists. We characterize the methods of Marine Data Science as a toolbox including skills from their two parental sciences. All of these aim to analyze and interpret marine data, which build the foundation of Marine Data Science.

Processing and assimilation of observation data for the hydrodynamics model of the Black Sea and the Sea of Azov

This work is aimed at using the marine data of the Shared Use Centre (SUC) “IKI-Monitoring” in the variational assimilation procedures of the Informational Computational System (ICS) “INM RAS - Black Sea”. SUC “IKI - Monitoring” is a tool for obtaining remote sensing observations on the Earth state. In the paper observation data information is given, data processing procedures are described, algorithms for the assimilation of the information received and several specific features of the numerical model used are presented. Results of the variational assimilation of two sets of observation data are presented and discussed. Numerical experiments have confirmed the possibility of using incomplete data from satellites in the problems of modelling the sea area.

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model.

A Robust UWSN Handover Prediction System Using Ensemble Learning

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical.

An integrated marine data collection for the German Bight – Part 2: Tides, salinity, and waves (1996–2015)

Marine spatial planning requires reliable data for, e.g., the design of coastal structures, research, or sea level rise adaptation. This task is particularly ambiguous in the German Bight (North Sea, Europe) because a compromise must be found between economic interests and biodiversity since the environmental status is monitored closely by the European Union. For this reason, we have set up an open-access, integrated marine data collection for the period from 1996 to 2015. It provides bathymetry, surface sediments, tidal dynamics, salinity, and waves for the German Bight and is of interest to stakeholders in science, government, and the economy. This part of a two-part publication presents data from numerical hindcast simulations for sea surface elevation, depth-averaged current velocity, bottom shear stress, depth-averaged salinity, wave parameters, and wave spectra. As an improvement to existing data collections, our data represent the variability in the bathymetry by using annually updated model topographies. Moreover, we provide data at a high temporal and spatial resolution (Hagen et al., 2020b); i.e., numerical model results are gridded to 1000 m at 20 min intervals (https://doi.org/10.48437/02.2020.K2.7000.0004). Tidal characteristic values (Hagen et al., 2020a), such as tidal range or ebb current velocity, are computed based on numerical modeling results (https://doi.org/10.48437/02.2020.K2.7000.0003). Therefore, this integrated marine data collection supports the work of coastal stakeholders and scientists, which ranges from developing detailed coastal models to handling complex natural-habitat problems or designing coastal structures.