Data Fusion Of Multiple Sources Research Articles

Application of machine learning to large hail prediction - The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5

This study presents a concept for coupling remote sensing data and environmental variables with machine learning techniques for the prediction of large hail events. In particular, we want to address the following question: How would one improve the performance of large hail warnings / forecasts if thermodynamic and kinematic parameters derived from a numerical weather prediction model are combined with real-time remote sensing data? For this purpose, POLRAD radar reflectivity, EUCLID lightning detection data, and convective indices calculated from the ERA5 reanalysis are combined and then compared with large hail reports from Poland (2008–2017). The data fusion of multiple sources, coupled with the machine learning approach, makes it possible to greatly improve the robustness of large hail prediction compared to any single product commonly used in operational forecasting. This is especially noticeable with the reduced number of false alarms. Although the created machine learning models are mainly driven by radar reflectivity, composite thermodynamic and kinematic indices such as Hail Size Index (HSI), Significant Hail Parameter (SHIP), Large Hail Parameter (LGHAIL), and WMAXSHEAR provide an added value to a model's performance. The accuracy achieved by a random forest model brings with it encouraging prospects for future research with respect to operational forecasters (who may fill in the gaps within NWP-derived data with remotely sensed measurement) and climatological studies that aim to investigate past and future changes in severe weather occurrences.

Applying OGC Standards to Develop a Land Surveying Measurement Model

The Open Geospatial Consortium (OGC) is committed to developing quality open standards for the global geospatial community, thus enhancing the interoperability of geographic information. In the domain of sensor networks, the Sensor Web Enablement (SWE) initiative has been developed to define the necessary context by introducing modeling standards, like ‘Observation & Measurement’ (O&M) and services to provide interaction like ‘Sensor Observation Service’ (SOS). Land surveying measurements on the other hand comprise a domain where observation information structures and services have not been aligned to the OGC observation model. In this paper, an OGC-compatible, aligned to the ‘Observation and Measurements’ standard, model for land surveying observations has been developed and discussed. Furthermore, a case study instantiates the above model, and an SOS implementation has been developed based on the 52° North SOS platform. Finally, a visualization schema is used to produce ‘Web Map Service (WMS)’ observation maps. Even though there are elements that differentiate this work from classic ‘O&M’ modeling cases, the proposed model and flows are developed in order to provide the benefits of standardizing land surveying measurement data (cost reducing by reusability, higher precision level, data fusion of multiple sources, raw observation spatiotemporal repository access, development of Measurement-Based GIS (MBGIS)) to the geoinformation community.

Information Service System Of Agriculture IoT

Internet of Things (IoT) was faced with some difficulties which contained mass data management, various standards of object identification, data fusion of multiple sources, business data management and information service providing. In China, some safety monitoring systems of agricultural product always adopt centralized system architecture in which the data is stored concentratively. These systems could not be connected with or accessed by each other. This paper proposed an information system of agriculture Internet of Things based on distributed architecture. A distributed information service system based on IoT-Information Service, Object Naming Service, Discovery Service is designed to provide public information service including of capturing, standardizing, managing and querying of massive business data of agriculture production. A coding scheme for agricultural product, business location and logistic unit is provided for data identification. A business event model of agriculture IoT is presented for business data management. The whole system realizes the tracking and tracing of agricultural products, and quality monitoring of agriculture production. The implementation of this information service system is introduced.