A Machine Learning Framework for Multi-Hazard Risk Assessment at the Regional Scale in Earthquake and Flood-Prone Areas

Communities are confronted with the rapidly growing impact of disasters, due to many factors that cause an increase in the vulnerability of society combined with an increase in hazardous events such as earthquakes and floods. The possible impacts of such events are large, also in developed countries, and governments and stakeholders must adopt risk reduction strategies at different levels of management stages of the communities. This study is aimed at proposing a sound qualitative multi-hazard risk analysis methodology for the assessment of combined seismic and hydraulic risk at the regional scale, which can assist governments and stakeholders in decision making and prioritization of interventions. The method is based on the use of machine learning techniques to aggregate large datasets made of many variables different in nature each of which carries information related to specific risk components and clusterize observations. The framework is applied to the case study of the Emilia Romagna region, for which the different municipalities are grouped into four homogeneous clusters ranked in terms of relative levels of combined risk. The proposed approach proves to be robust and delivers a very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling at the regional scale.

A Theoretical Framework for Multi-Hazard Risk Mapping on Agricultural Areas Considering Artificial Intelligence, IoT, and Climate Change Scenarios

This work proposes a data-driven theoretical framework for addressing: (i) extreme climate events prediction through multi-hazard risk mapping using remote sensing, artificial intelligence, and hydrological models, considering multiple hazards; and (ii) environmental monitoring using on-site data collection and IoT technologies. The framework considers the possibility of evaluating multiple climate change scenarios for improving decision-making in terms of Government policies and farm planning. Its main requirements are gathered based on a literature review. Several essential metrics that can be evaluated, considering both supervised and unsupervised metrics and key performance indicators considering the triple bottom line aspects, are also proposed. The framework also adopts multi-hazard (considering several hazards) and multi-risk (considering several relevant stakeholders) aspects and can be used to simulate different scenarios, an essential task for improving decision-making.

Influence of the Seismic Wave Velocity of the Damage Zone on Near-Field Ground Motions

In burst-prone deep underground engineering, seismic waves generated from a near-field ground motion event may play a critical role in causing localized rockburst damage. Accurate estimation of near-field ground motions around excavations is important for seismic hazard risk assessment and dynamic rock support design in underground engineering. During the excavation of an underground cavern, stress redistribution in the surrounding rock leads to the formation of damage zones, including the excavation damage zone (EDZ) and excavation fracture zone (EFZ). The poor properties of the rock in the damage zones cause the wave velocities of the rock mass to decrease and the dynamic wave interaction to change, thereby affecting the ground motions around the excavation. This paper studies the near-field ground motion behavior and reveals the control effect of the seismic wave velocity in the damage zones on the near-field ground motions by the aid of the finite fracturing source model (FFSSM). The research results provide a new knowledge of the influence of excavation disturbance on the ground motion distribution around the excavation, and provide new ideas for the seismic hazard risk assessment and prevention in underground engineering.