This paper combines research and commentary to reinforce the importance of integrating hazard interactions and interaction networks (cascades) into multi-hazard methodologies. We present a synthesis of the differences between multi-layer single-hazard approaches and multi-hazard approaches that integrate such interactions. This synthesis suggests that ignoring interactions between important environmental and anthropogenic processes could distort management priorities, increase vulnerability to other spatially relevant hazards or underestimate disaster risk. In this paper we proceed to present an enhanced multi-hazard framework through the following steps: (i) description and definition of three groups (natural hazards, anthropogenic processes and technological hazards/disasters) as relevant components of a multi-hazard environment, (ii) outlining of three types of interaction relationship (triggering, increased probability, and catalysis/impedance), and (iii) assessment of the importance of networks of interactions (cascades) through case study examples (based on the literature, field observations and semi-structured interviews). We further propose two visualisation frameworks to represent these networks of interactions: hazard interaction matrices and hazard/process flow diagrams. Our approach reinforces the importance of integrating interactions between different aspects of the Earth system, together with human activity, into enhanced multi-hazard methodologies. Multi-hazard approaches support the holistic assessment of hazard potential and consequently disaster risk. We conclude by describing three ways by which understanding networks of interactions contributes to the theoretical and practical understanding of hazards, disaster risk reduction and Earth system management. Understanding interactions and interaction networks helps us to better (i) model the observed reality of disaster events, (ii) constrain potential changes in physical and social vulnerability between successive hazards, and (iii) prioritise resource allocation for mitigation and disaster risk reduction.

Hazard Interactions and Interaction Networks (Cascades) within Multi-Hazard Methodologies

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems.

https://www.mdpi.com/2071-1050/13/10/5578/pdf

Smart City Ontologies and Their Applications: A Systematic Literature Review

BERT-based chinese text classification for emergency management with a novel loss function

This paper proposes an automatic Chinese text categorization method for solving the emergency event report classification problem. Since bidirectional encoder representations from transformers (BERT) has achieved great success in natural language processing domain, it is employed to derive emergency text features in this study. To overcome the data imbalance problem in the distribution of emergency event categories, a novel loss function is proposed to improve the performance of the BERT-based model. Meanwhile, to avoid the impact of the extreme learning rate, the Adabound optimization algorithm that achieves a gradual smooth transition from Adam to SGD is employed to learn parameters of the model. To verify the feasibility and effectiveness of the proposed method, a Chinese emergency text dataset collected from the Internet is employed. Compared with benchmarking methods, the proposed method has achieved the best performance in terms of accuracy, weighted-precision, weighted-recall, and weighted-F1 values. Therefore, it is promising to employ the proposed method for real applications in smart emergency management systems.

BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function.

Knowledge, semantics and AI for risk and crisis management

With the acceleration of urbanization, cascading disaster risks (CDR) as a typical risk mode have become the main threat to cities. After experiencing several cascading disasters, such as typhoon Lekima, how to clarify the basic features of CDR and achieve risk modelling has turned to be increasingly significant for building resilient city. However, the complexity of CDR brings about the difficulty to quickly map such risk mode depending entirely on expertise. Therefore, this paper attempts to provide a CDROntology system built by concepts and relations, and make full use of the historical cases to drive the modelling of the target CDR with case‐based reasoning. Firstly, we describe the basic structure and content of CDR and give a three‐level CDROntology system with the explanation of modelling primitives. Then, taking CDROntology system as the basis, a case‐driven selection process is proposed to provide the modelling source for the target CDR. Furthermore, set covering and manual correction methods are adopted to model the evolutionary risk chain and the specific risk scenario of the target case. Finally, a case study is given to illustrate the use of CDROntology system and case‐driven method for building a predictive risk model in typhoon‐triggered cascading disasters.

Xiangyang Li

Papers

Improving emergency preparedness to cascading disasters: A case‐driven risk ontology modelling