(Virtual Showroom): Artificial Intelligence (AI) is advancing globally. Many studies have shown the benefits of AI in many spheres of industry. One such sphere is environmental compliance monitoring, such as live monitoring of air quality stations, CFC emissions, and water quality probes.
AI can benefit environmental compliance monitoring by analysing data more efficiently (Satellite imaging, aerial photography, and drone footage just to name a few) by analysing historical data and comparing it to current data. This can be utilised to predict future risks and exceedances allowing for the early identification of potential environmental compliance issues (Verma, 2023).
Analysing large volumes of data can be time consuming particularly with tight deadlines and regular follow up monitoring required. AI is able to process data more efficiently and in a shorter time span. However, the environmental compliance officer will still need to review and verify the results produced by the AI to ensure that it is a true reflection. (Denney, 2022). To train AI in analysis of data of remote sensing is to use maximum likelihood classification (ML), support vector machine (SVM), and iterated conditional modes (ICM) and these three methods are applied to Landsat 5 Thematic Mapper (TM) (Keuchel, et al., 2003). ML and SVM are non-contextual methods and ICM exploits spatial context by using a Markov random field (Keuchel, et al., 2003). This is one method AI can be used to monitor changes in environmental conditions and identify environmental non-compliances.
Once the AI has been trained in analysing environmental compliance data, it will reduce errors in data representation. Although AI can be a powerful means of processing large volumes of data, a human element will still be required in terms of making realistic and suitable recommendations where the AI has identified potential environmental non-compliances. Utilising AI as a means of processing large volumes of data allows for quicker turnaround time of reports and presentation of findings and recommendations(Denney, 2022).
Once trained and programmed to site-specific projects, AI is able to predict potential environmental non-compliances with respect to local legislation such as exceedances of air or water quality limits (Verma, 2023). Early identification allows developers to respond to potential non-compliances before they become major issues thereby reducing the potential of being in contravention of local legislation and potentially facing legal charges or fines (Verma, 2023).
Using AI technology can allow data collection in areas that are inaccessible to humans which can allow more effective compliance monitoring such as the use of drones (Verma, 2023).
ENVASS boasts a highly proficient and seasoned Compliance Monitoring Team, consistently prepared to provide invaluable assistance. Our team possesses extensive expertise in local legislation and compliance monitoring, ensuring the seamless fulfilment of your specific compliance monitoring needs.