出版者・発行元：Geosciences Switzerland  

Worldwide, landslides claim many lives each year, with an average of 162.6 deaths reported in Japan from 1945 to 2019. There is growing concern about a potential increase in this number due to climate change. The primary source of shallow and rapid landslides within watersheds is the 0-order basins, which are located above the 1^st order drainage system. These active geomorphological locations govern the frequency of mass movement. Despite the recognition of their importance, little attention has been paid to the role of 0-order basins in initiating landslides. Drainage systems can be extracted using the Digital Elevation Model (DEM) in GIS software. However, the effect of DEM resolution on the extraction of 1^st order basins remains unexplained. This research develops an algorithm to assess the impact of DEM resolution on the extraction of first-order basins, channel head points, and the identification of approximate 0-order basins. The study includes algorithms to evaluate the correlation between DEM resolution and 1^st order drainage system extraction using fuzzy classification techniques for approximate 0-order basins. The algorithm was applied in Toho Village, Fukuoka, Japan, defining the most appropriate DEM and stream definition threshold with an 86.48% accuracy and ±30 m error margin for channel head points. Critical slip surfaces were identified inside the 0-order basins and validated with a landslide inventory map with a 91% accuracy. The developed algorithms support hazard management and land use planning, providing valuable tools for sustainable development.

DOI： 10.3390/geosciences14080223

Web of Science

Scopus

出版者・発行元：Lecture Notes in Civil Engineering  

Heavy rainfall is a frequent and widespread severe weather hazard that may cause flood damage and human casualties. Since heavy rainfall is a progressive disaster, its scale and hazardous areas can be foreseen beforehand. Therefore, evacuating people from hazardous buildings to shelters in advance is an efficient effort to reduce casualties, but a scientific basis is still required. This paper proposes an approach for assessing each building’s evacuation vulnerability based on predicted evacuation time, aiming to support evacuation decision-making under heavy rainfall. As such, this paper applies Dijkstra’s algorithm to find the evacuation route from each building to accessible shelters. Moreover, a prediction model based on the random forest algorithm is developed to estimate their time-varying evacuation time. Road spatial and temporal characteristics that may affect evacuation time are used when developing the model. Finally, the proposed approach is implemented in Joso City, Japan, to verify its feasibility. As a result, the proposed approach accurately predicts and visualizes the evacuation time between each building and its optimal evacuation shelter. It also visually identifies the hard-to-evacuate buildings. The results indicate that the proposed approach can effectively reflect evacuation vulnerability and support heavy rainfall evacuation decision-making, which proves its validity and practicality.

DOI： 10.1007/978-981-99-9219-5_2

Web of Science

Scopus

出版者・発行元：Lecture Notes in Civil Engineering  

Improving the disaster risk reduction literacy of residents is crucial for reducing the damage caused by serious disasters. This study aimed to investigate the impact of developing a community disaster management plan (CDMP) on residents’ disaster risk reduction literacy. To formulate the CDMP, a disaster risk communication workshop was conducted to collect information on existing disasters and discuss disaster risks in the community. The results were compiled into a risk map and timeline to support proactive disaster management activities by residents. The study assessed the effect of participation in the workshop on disaster risk reduction literacy, using a post-workshop questionnaire to survey participants. Propensity score analysis was employed to infer causal effects. The results showed that full participation in the workshop significantly increased participants’ understanding of disaster threats and improved disaster risk reduction literacy.

DOI： 10.1007/978-981-99-9219-5_33

Web of Science

Scopus

記述言語：日本語   出版者・発行元：一般社団法人 地域安全学会  

<p>During heavy rainfall disasters, quantitative assessments of disaster risk are necessary to support municipalities' decision to issue evacuation information and residents' decision on whether to evacuate has been noted. In this research, two disaster risk assessment methods are proposed for municipalities and residents, based on the evacuation time. Afterwards, in a flood scenario of Joso City, the lead-time of municipalities' evacuation decisions has been calculated by the municipality-oriented assessment method. Meanwhile, the temporal variation of evacuation difficulty of each resident has been quantified by resident-oriented assessment method. As a result, the proposed methods have been considered feasible to support the evacuation decisions of municipalities and residents.</p>

DOI： 10.11314/jisss.43.9

CiNii Research

出版者・発行元：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics  

This paper introduces a new problem called the Robust Maximum Satisfiability problem (R-MaxSAT), as well as its extension called the Robust weighted Partial MaxSAT (R-PMaxSAT). In R-MaxSAT (or R-PMaxSAT), a problem solver called defender hopes to maximize the number of satisfied clauses (or the sum of their weights) as the standard MaxSAT/partial MaxSAT problem, although she must ensure that the obtained solution is robust (In this paper, we use the pronoun “she” for the defender and “he” for the attacker). We assume an adversary called the attacker will flip some variables after the defender selects a solution. R-PMaxSAT can formalize the robust Clique Partitioning Problem (robust CPP), where CPP has many real-life applications. We first demonstrate that the decision version of R-MaxSAT is Σ2P -complete. Then, we develop two algorithms to solve R-PMaxSAT, by utilizing a state-of-the-art SAT solver or a Quantified Boolean Formula (QBF) solver as a subroutine. Our experimental results show that we can obtain optimal solutions within a reasonable amount of time for randomly generated R-MaxSAT instances with 30 variables and 150 clauses (within 40 s) and R-PMaxSAT instances based on CPP benchmark problems with 60 vertices (within 500 s).

DOI： 10.1007/978-3-031-20862-1_2

Web of Science

Scopus