Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning

Abstract

Machine learning (ML) algorithms can prove to be instrumental in certain complex ill-conditioned systems when inserted as a middle layer to interface low-level hardware, such as sensors and actuators, and high-level decision-making kernels. Such an interface provides a secondary, or supervisory, conditioning layer that would enhance the system's robustness in the face of various types of uncertainties and disturbances. This article elaborates how ML can leverage the solution of a contemporary problem related to the security of maritime domains.The worldwide "Illegal, Unreported, and Unregulated" (IUU) fishing incidents have led to serious environmental and economic consequences which involve drastic changes in our ecosystems in addition to financial losses caused by the depletion of natural resources. The term "illegal" refers either to contravening the fishing regulations or conducting fishing activities unlawfully under the jurisdiction of a certain maritime territory. In this context, "unreported" indicates the failure to report to the relevant authorities, while "unregulated" describes fishing activities in areas with no applicable measures to control the catch. The Fisheries and Aquatic Department (FAD) of the United Nation's Food and Agriculture Organization (FAO) issued a report which indicated that the annual losses due to IUU fishing reached $25 Billion [1]. This imposes negative impacts on the future-biodiversity of the marine ecosystem and domestic Gross National Product (GNP). Maritime Domain Awareness (MDA) is concerned with the situational awareness relevant to the activities that have direct or indirect impacts on the operational, organizational, economical, and safety sides of the maritime domains [2,3]. The assets in MDA support different missions, such as monitoring, risk assessment, and management. Hence, robust interception mechanisms are increasingly needed for detecting and pursuing the unrelenting illegal fishing incidents in maritime territories.

Crime Prediction and Monitoring in Porto, Portugal, Using Machine Learning, Spatial and Text Analytics

“…Machine learning approaches have been widely applied in different fields, such as urban science, transport and pedestrian flow prediction, healthcare, biology, archeology, finance and even arts [38,39]. They have been used to monitor illegal activities [40,41] and to model and predict crime, with authors often comparing various methods [42][43][44][45][46].…”