Natural Disasters and Risk Management

In recent years, natural disasters have compelled public authorities, organizations, and citizens to increase their efforts in properly planning and implementing effective risk management procedures. Accordingly, in literature contemporary concepts such as natural disaster risk management and crisis management emerged. Therefore, the chapter aims to shed light on the significance of natural disaster risk management and crisis management in the development of an effective societal system by its transformation and to point out the positive and negative factors influencing these management activities. The authors will firstly give an overview of these two concepts, their elements, and development phases, and afterward, the investigation of possible positive and negative factors of natural disaster risk management will be introduced. The chapter will make a significant contribution to filling the gap in the literature on mitigating the influence of natural disasters and risk management.

A Simple Way to Reduce the Risk of Being Exposed to COVID-19 Using Instruments for Children

Background. Children need to get protection during the Covid-19 pandemic, in accordance with Presidential Regulation 12/2020 on The Determination of Non-Natural Disasters. The purpose of the research is to develop non-natural disaster risk instruments for children. Research methods. This type of research is Research and Development. The participant population is divided into 3 roles, Focussed group discussion participants (20 people), instrument testing (267 respondents) and experts (methodology experts and disaster experts). Instrument development is carried out in 5 (five) stages, namely: 1) instrument evaluation; 2) development; 3) experiment; 4) socialization; 5) recommendations. The results showed that from 32 valid items, 25 items were all reliable because the Cronbach alpha value was above 0.7. Not valid for 7 items. The results of the instrument trial of 267 children, who had the potential to be exposed to Covid-19 were 69% who were not exposed to 31%. Of the 69%, strong exposure is 2%, high exposure is 15% and medium exposure is 83%. Conclusion: The developed non-natural disaster risk instrument can be used to assess disaster risk for children. The instrument works well, has efficiency, functionality and usability. Outputs: 1) disaster risk instruments for children; 2) intellectual property rights; 3) publication of international journals 4) monograph books.

FRAMEWORK FOR PLANNING CAPACITIES TO MAXIMIZE THE FEASIBLE RESILIENCE OF NETWORK INFRASTRUCTURE SYSTEMS

Infrastructures are connected between themselves and their connections form a complex network. Disaster impacted infrastructures have a reduced level of serviceability. The impact of the reduction of serviceability of disaster-affected infrastructures cascades through the entire infrastructure network. The recovery capacity of the network governs the recovery curve of a disaster-impacted infrastructure network system. Moreover, the speed of recovery of the infrastructure network from a post-disaster condition to the pre-disaster condition dictates the resilience of the system. Building these capacities requires spending money. The extent of resilience that can be afforded under a given budget situation is termed as the feasible resilience. This paper presents a framework for planning the capacities so that the feasible resilience of the network can be maximized under a budget situation. To achieve the said objective, the recovery time of a disaster-affected infrastructure network has been minimized. The outcome is the optimal restorative capacities the network needs to have so that the feasible resilience is maximized under a specified budget condition. The outcomes can be used by the decision-makers in natural disaster risk mitigation planning.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

The Impact of Financial Preparation and Disaster Experience on Households’ Disaster Risk Perception: Empirical Evidence from Quang Binh Province, Vietnam

This paper is aimed at analysing the impacts of financial preparation and disaster experience on households’ disaster risk perception, including perceptions of likelihood and severity in Quang Binh Province of Vietnam, one of the areas strongly affected by natural disasters and climate change. With the data from direct surveying 308 households in Quang Binh province, the research methodology includes Cronbach’s Alpha, EFA and OLS regression models. The key findings are: First, disaster experience has positive impact on natural disaster risk perception. Second, financial preparation has negative impact on natural disaster risk perception. Third, the risk of natural disasters in Quang Binh Province are increasing and unpredictable due to rapid economic growth and urbanization. This fact requires the Government, provincial commitees, and stakeholders to go beyond traditional coping methods, implement more customized policies and specific actions to try to reduce the risks of natural disasters. Keywords: disaster risk, disaster risk perception, financial preparation, disaster experience.