Environmental Hazards: A Coverage Response Approach

Abstract

The rapid rise and implementation of Smart Systems (i.e., multi-functional observation and platform systems that depict settings and/or identify situations or features of interest, often in real-time) has inversely paralleled and readily exposed the reduced capacity of human and societal systems to effectively respond to environmental hazards. This overarching review and essay explores the complex set of interactions found among Smart, Societal, and Environmental Systems. The resulting rise in the poorly performing response solutions to environmental hazards that has occurred despite best practices, detailed forecast information, and the use and application of real-time in situ observational platforms are considered. The application of Smart Systems, relevant architectures, and ever-increasing numbers of applications and tools development by individuals as they interact with Smart Systems offers a means to ameliorate and resolve confounding found among all of the interdependent Systems. The interactions of human systems with environmental hazards further expose society’s complex operational vulnerabilities and gaps in response to such threats. An examination of decision-making, the auto-reactive nature of responses before, during, and after environmental hazards; and the lack of scalability and comparability are presented with regard to the prospects of applying probabilistic methods, cross-scale time and space domains; anticipated impacts, and the need to account for multimodal actions and reactions—including psycho-social contributions. Assimilation of these concepts and principles in Smart System architectures, applications, and tools is essential to ensure future viability and functionalities with regard to environmental hazards and to produce an effective set of societal engagement responses. Achieving the promise of Smart Systems relative to environmental hazards will require an extensive transdisciplinary approach to tie psycho-social behaviors directly with non-human components and systems in order to close actionable gaps in response. Pathways to achieve a more comprehensive understanding are given for consideration by the wide diversity of disciplines necessary to move forward in Smart Systems as tied with the societal response to environmental hazards.

Smart Cities—A Structured Literature Review

“…Other quantitative techniques being used in smart cities to analyze benefits and costs include k-means clustering algorithms, which can analyze big datasets and convert them into a graph with three seasons [66]. Probabilistic methods, multimodal actions/reactions, and constructs examining city heat amplification are used to analyze the anticipated impacts of environmental hazards on individuals or populations [67]. Hadoop with Spark, VoltDB, or Storm is used for real-time processing of IoT data, MapReduce programming is used for analyzing offline historical datasets, and machine learning algorithms such as regression models and decision trees to identify patterns from large amounts of collected data [64].…”

“…Quality of Life (QoL): This metric measures the well-being of citizens in terms of health, safety, education, and other factors that contribute to their overall quality of life. Smart city projects that prioritize QoL may include initiatives to improve healthcare access, reduce crime, or promote public transportation [2,16,46,64,67,68].…”