In order to calculate specific Criteria and Indicators (C&Is), it is first necessary to obtain data that (potentially) reflects the environment in which the business operates and responds to change. This ‘environmental data’ can be obtained using sensing and /or computer modelling techniques.
Sensing includes using direct measurements with monitoring equipment or remote sensing using aircraft, satellites, and drones. Environmental data can be obtained from national and local government websites, examples include weather, river flow, water quality and so forth.
Computer modelling can apply to the modelling of air, land, and sea to capture the behaviour of fluids and strata. On land, for example, river flow and groundwater can be simulated using rainfall changes as an input, or simulate ground vibrations caused by the propagation of seismic waves from a fault. To do this effectively requires experts to fully clarify the assumptions and scope of application of the environmental data.
A wide range of methods have been devised in more recent years to extract features from ever-changing environmental data based on increased computation availability. Examples include multi-objective optimisation for competing stakeholders, quantification of uncertainty and sensitivity structures from environmental data, and surrogate models for learning relationships between input and output data in detailed simulation models.
The integration of sensing, modelling, and peripheral technologies, enable a smooth link between data collection and C&I assessment, visualisation, and data storage. Furthermore, it permits the iterative PDCA cycle and is highly important within environmental data science and achieving high quality data acquisition.
Blue Earth Security is committed to developing unique technologies to support environmental data science in the 21st century.