Disaster Management Plans Research Articles

Future of Earthquake-Based Disaster Logistics and Futuristic Approaches

Disasters that occur in the world can be caused by natural as well as human-induced factors. These disasters cause damage to cities, villages, and inhabited areas, and also result in loss of life and property. Accurately and effectively planned disaster logistics management activities are crucial during these moments when life comes to a halt. Today, it is evident that cities suffer damage or even face extinction due to deficiencies in planning, coordination, and opportunities within these practices. To mitigate the consequences of disasters and minimize damage, it is essential to always be prepared for such events and manage them well before, during, and after they occur. Disaster logistics practices should be dynamic and tailored to the specific event, suitable for addressing the situation at hand. Each disaster requires a unique set of solutions that depend on the area and geography affected. Therefore, disaster logistics must adopt and develop new approaches with a sustainable perspective. Given that earthquakes are among the disasters that occur frequently in our country, there is a critical need to create earthquake-based disaster management and action plans. Looking at disaster logistics from a futuristic perspective, it is clear that digital transformation technologies can have a significant and beneficial impact, making disaster logistics services more efficient, innovative, and solution oriented. The study conducted a literature review within the framework of qualitative research methodology, providing a detailed analysis of the role of digital transformation technologies in earthquake logistics processes. The literature review was conducted by reviewing relevant academic articles, reports, books, and scientific research. Special emphasis was placed on studies published in the last 10 years, focusing on the intersection of disaster logistics and digital transformation technologies.

Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Frequent flooding has become a persistent issue in floodplain regions, causing significant disasters during each rainy season due to insufficient disaster management planning. This study proposes a methodology to prioritize flood susceptibility areas at the district level and identify suitable sites for flood shelters using a combination of machine learning algorithms and multi-criteria analysis, supported by geospatial technology. Flood shelter suitability mapping was conducted using the Analytical Hierarchy Process (AHP), while flood susceptibility zones were assessed using four different machine learning models: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. The integration of machine learning models with the AHP technique is vital in situations where conventional numerical models face challenges due to limited data, such as river discharge and water levels. The methodology includes a multicollinearity assessment to ensure the independence of selected flood-causing factors, information gain ratio to identify the most influential factors, Spearman's rho test to verify correlations between the machine learning models, and ROC-AUC along with statistical regression for validating the accuracy of the flood susceptibility maps. The findings indicate that the SVM algorithm, given its strong performance and effective training datasets, is recommended for areas with similar physical characteristics. The district-wise priority map generated from the weighted results of flood susceptibility assessments will be useful for flood management and mitigation strategies. Additionally, the study found that applying the AHP technique to determine flood shelter suitability, after assessing flood-prone areas, enhanced the efficiency of the flood management process. This research offers valuable insights for authorities to better address flooding and improve flood prevention and management efforts in floodplain regions, contributing to broader climate change adaptation strategies.

An Examination of the Decision of Microentrepreneurs to Remain in Urban Flood-prone Areas in Marikina City, Metro Manila (NCR), Philippines

Despite the impact of urban flooding on businesses, the choice of microentrepreneurs continuing to stay in flood hazard areas runs counterintuitive to the principles of profitability and sustainability albeit both are the most vulnerable aspects of the business sectors. Existing literature has provided substantial knowledge on the factors affecting an entrepreneur’s choice of operating location but does not go deeper into why entrepreneurs decide to remain and operate in frequently flooded urban areas. A multi-stage cluster sampling technique was used in this study to establish an adequate representation of microentrepreneurs in flood-prone areas of Marikina City in Metro Manila, Philippines, using various statistical tools to evaluate decision factors. The research revealed that component factors such as economic considerations, the microentrepreneur’s socio-spatial relations, and experiences and perception of resilience influence the decision to remain in urban flood-prone areas. Moreover, specific sub-factors under each component factor were identified, which can be grouped into ‘pre-existing’ and ‘learned’ categories, introducing a new location decision model attuned to the dynamics that exists between microentrepreneurs and a natural disaster such as urban flooding. The findings of the study may guide policymakers and urban planners in designing targeted intervention measures that translate to more micro enterprise-responsive disaster risk reduction and management plans. Moreover, by treating microenterprises as separate from the traditional occupations, communities might appreciate the crafting of local land use plans which would successfully improve the overall resilience of this sector in urban flood-prone areas.

Improving the UNet with channel attention mechanism for Taihu Lake water body recognition

Abstract Accurate recognition of surface water bodies is important for disaster management and resource planning. To explore a higher-precision water body recognition method, propose an improved channel attention mechanism UNet (ECA-UNet) water body recognition model based on Sentinel-2A remote sensing images. Utilizing ECA-UNet, UNet, PSPnet, and DeeplabV3, as well as the traditional normalized water body index (NDWI), recognize the water body in the Taihu Lake area. Through comparative analysis, the following main conclusions are drawn: (1) The recognition accuracy of the deep learning network model is better than the traditional NDWI method, among which the ECA-UNet network model has the highest accuracy of 98.67%, and the lowest recognition accuracy of NDWI is 89.06%. (2) The ECA-UNet network model can effectively extract water body recognition targets, has a better recognition effect on local features of water bodies, and can improve the water body extraction effect. The above results show that the improved channel attention mechanism ECA-UNet has the highest accuracy in water body recognition in Taihu Lake, which provides a reference for subsequent research and applications in this field.

NSEMBLE FINE TUNED MULTI LAYER PERCEPTRON FOR PREDICTIVE ANALYSIS OF WEATHER PATTERNS AND RAINFALL FORECASTING FROM SATELLITE DATA

The accurate prediction of weather patterns and rainfall forecasting is critical for various sectors, including agriculture, disaster management, and water resource planning. Traditional models often struggle to capture the complex interactions between atmospheric variables, particularly when integrating diverse types of satellite data (binary, categorical, and numerical). To address this challenge, an ensemble fine-tuned multi-layer perceptron (MLP) model is developed, combining the strengths of multiple machine learning techniques for more robust predictions. The primary problem is the difficulty in handling mixed data types while maintaining high prediction accuracy. Satellite data, including binary indicators (e.g., cloud presence), categorical features (e.g., cloud types), and numerical variables (e.g., temperature, humidity, and wind speed), provide rich information but require specialized processing for effective forecasting. The proposed method involves fine-tuning an ensemble of MLP models with backpropagation, dropout regularization, and batch normalization to reduce overfitting and enhance generalization. The ensemble integrates predictions from individual MLP models, each trained on different subsets of features (binary, categorical, numerical). This technique allows the model to leverage complementary strengths and produce more accurate rainfall forecasts. Satellite data is preprocessed and normalized before training, and categorical variables are one-hot encoded to ensure compatibility with the MLP architecture. Results from testing on historical satellite weather datasets demonstrate significant improvements in forecast accuracy. The ensemble MLP achieved an accuracy of 91.3%, with a precision of 90.7%, recall of 89.5%, and an F1-score of 90.1%. The model performed exceptionally well in identifying critical rainfall events, reducing false positives by 12% compared to traditional models.

Remote sensing assessment of wildfire using high-resolution PlanetScope satellite observations: A case study on Co Tien Mountain, Nha Trang City, Vietnam

In this study, high spatial resolution (3 m) PlanetScope (PS) imagery was utilized to map burned areas caused by a wildfire occurring on January 10, 2024, on Co Tien Mountain in Nha Trang city, Khanh Hoa province, South Central Coast of Vietnam. A pre-fire image, acquired ten days earlier, on December 31, 2023, and a post-fire one, acquired nearly one month after, on February 04, 2024, were used to create pre- and post-fire Normalized Difference Vegetation Index (NDVI) maps of the study area, then the difference of NDVI (dNDVI). A threshold (T = 0.20), proposed by the author, was applied to the histogram of the dNDVI product to classify the study area into two clusters: burned pixels (dNDVI > T) and unburned pixels (dNDVI <= T). Classification results estimate that a total of 16.11 ha of grass, reeds, small shrubs and vegetation have been burned out during the wildfire. A field trip is required to map the burned areas using unmanned aerial vehicles (UAVs) for an accurate validation of results derived purely from PS satellite observations. Although lacking a ground truth dataset for validation is a significant limitation, the proposed approach remains beneficial for local managers and decision-makers. It enables the rapid assessment of damages caused by small wildfires and provides essential data for effective disaster management and recovery planning, particularly in remote areas.

Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India

Landslide susceptibility mapping is vital for disaster management and sustainable land-use planning. This research was conducted in Wayanad, Kerala, India, to identify landslide susceptible zones. The study used large geospatial datasets, such as elevation, slope, aspect, curvature, stream power index, topographic wetness index, land use and land cover, rainfall, flow accumulation, geology, and geomorphology. It is followed by the application of various machine learning and deep learning models such as the support vector machine, artificial neural networks, logistic regression, random forest, gradient boosting machine, recurrent neural networks long short-term memory, and deep neural network models to map the landslide susceptible zones. The model was trained and validated using the landslide inventory map, which contains 298 sites of landslides. The random forest model, with 97 % accuracy, performed best. It is possible to effectively mitigate landslides and plan long-term land use by identifying hazardous zones within the study region.

Assessing the impact of the 2021 flood event on the archaeological heritage of the Rhineland (Germany)

BackgroundArchaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage.ResultsDuring fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage.ConclusionsThe study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans.

Deep-TCP: Multi-source data fusion for deep learning-powered tropical cyclone intensity prediction to enhance urban sustainability

Tropical cyclones (TC) exert a profound impact on cities, causing extensive damage and losses. Thus, TC Intensity Prediction is crucial for creating sustainable cities as it enables proactive measures to be taken, including evacuation planning, infrastructure reinforcement, and emergency response coordination. In this study, we propose a Deep learning-powered TC Intensity Prediction (Deep-TCP) framework. In particular, Deep-TCP contains a data constraint module for fusing data features from multiple sources and establishing a unified global representation. To capture the spatiotemporal attributes, a Spatial-Temporal Attention (ST-Attention) module is built to distill insights from environmental variables. To improve the robustness and stability of the predictions, an encoder-decoder module that utilizes the ConvGPU unit is introduced to enhance feature maps. Then, a novel feature enhancement module is built to bolster the generalization capability and solve the dependency attenuation. The results demonstrate that the Deep-TCP framework significantly outperforms various benchmarks. Additionally, it effectively predicts multiple TC categories within the 6–24 h timeframe, showing strong capability in predicting changing trends. The reliable prediction results are potentially beneficial for disaster management and urban planning, significantly enhancing urban sustainability by improving preparedness and response strategies.

DEVELOPING A LOCALIZED OPTIMAL FLOOD BARRIER IN THE PHILIPPINES

In the Philippines, flood is a usual experience due to its geographical location near the equator. This calls for governmental units to establish flood control protocols that utilize equipment to effectively mitigate the impact of the floods. However, the existing protocols lack proper equipment integrated in their flood protocols that effectively handle flood occurrences. This research aims to improvise the current practices of the Philippine local government units in combating flood by proposing the optimal flood barrier design to be deployed for flood control and damage mitigation and investigate the optimal flood barrier for deployment. The methodology involves revisiting operational manuals of the target community of the Philippine governmental units and exploring existing flood barrier designs in literature and the market, whether in or out of the Philippines. Barriers designed in the literature and market were investigated, where there is a physical experiment conducted for the optimal design using physics concepts and past tests performed by studies. Results demonstrated the amount of force that can be resisted by the barriers, the amount of flood water until it floats, and the maximum velocity until barriers move. This is the first study to investigate the designs of flood barriers that are suitable for deployment and selects the optimal barrier for flood control for the Philippine setting while integrating it to the Philippine flood protocols. This research will be valuable to studies in regards with flood emergency disaster management planning.

Estimating a Coastal Compound Flood Standard for the Taehwa River Basin

The risk of coastal compound flood, caused by the interaction of rainfall and tides, has been increasing with sea level rise and changes in rainfall due to climate change. However, there is no costal compound flood study that considers the interaction of rainfall and tides in Korea. Therefore, this study applied the compound flood concept to the Taehwa river basin to estimate the compound flood standard for the Taehwa river basin. The result revealed that the coastal compound flood occurred when the rainfall intensity was greater than 22.42 mm/hr and the tide was greater than 0.71 m. In addition, the study found that typhoon plays a role in coastal compound flood by analyzing the distribution of meteorological and oceanographic factors during the compound flood events. Our results can be used to develop disaster management plans to prepare for the risk of compound flood.

Emergency Radiology in the First 24 h of Two Major Earthquakes on the Same Day and Radiologic Evaluation of Trauma Cases.

On 6 February 2023, two major earthquakes occurred in Turkey on the same day. More than 50,000 people died, and more than 100,000 people were injured in these earthquakes. The aim of this study is to contribute to disaster management plans by evaluating the functioning of a radiology department and the imaging examinations performed after this disaster. The functioning of the radiology clinic at Malatya Training and Research Hospital in the first 24 h after the earthquake was evaluated. The images of 596 patients who were admitted to Malatya Training and Research Hospital for earthquake-related trauma between 6 February 2023, at 4:17 a.m. and 7 February 2023, at 4:17 a.m., and who underwent radiography and computed tomography (CT) were retrospectively reviewed. The mean age of the patients was 37.3 ± 20.1 years. A total of 313 (52.5%) patients were male. The most frequently performed imaging test was a CT scan. In total, 437 (73.3%) of 596 patients underwent a CT scan. At least one body part was affected in 160 patients (26.8%). The most commonly affected regions were the thorax, vertebrae, and extremities. Thoracic findings were observed in 52 patients (32.5%), vertebral findings in 52 patients (32.5%), and extremity findings in 46 patients (28.7%). Fractures were the most common finding in our study. Of the 160 patients with pathologic findings, 139 (86.9%) had evidence of fractures. The role of radiology in disasters is important. When disaster preparedness plans are made, radiology departments should be actively involved in these plans. This will ensure the quick and efficient functioning of radiology departments.

Understanding the impact of natural disasters on children within fist hours and days after an event: A phenomenological study through the experience of nurses.

Children are a particularly vulnerable group in natural disasters such as earthquakes, and although they represent a difficult group to research in such situations, there appears to be a lack of literature investigating children's experiences in the immediate post-earthquake period. Experienced nurses can shed light on children's experiences. This study was conducted to examine the experiences of nurses caring for children affected by the earthquake in Kahramanmaraş, Turkey. This phenomenological study was conducted with nurses who provided care to children during the Kahramanmaraş earthquake between May and August 2023. The study sample consisted of 14 nurses selected using a purposive sampling technique. Data were collected through in-depth interviews by using the "Introductory Information Form" and "Semi-structured Interview Form" developed by the researchers. The interviews were subsequently analyzed using Colaizzi's method. The results revealed five themes under two main themes. Under the main theme of nurse, there are subthemes of chaos, helplessness-incompetence, while under the main theme of child, there are subthemes of unresponsiveness, intertwined needs, empathy, and care. The study also highlights the chaotic environment in the immediate aftermath of the earthquake, characterized by a high number of pediatric casualties and psychological trauma, and the nurses' feelings of helplessness as a result of being separated from their families. This study found that nurses caring for children in the earthquake had difficulties in managing children's hospital processes and they demonstrated an empathic approach by trying to communicate with the children in their care. The nurses stated that children should be supported psychologically as well as having their basic needs such as water, food, and shelter met. Health policymakers should be aware that in the early stages of major disasters such as earthquakes, children are not only physically injured but also have basic needs such as water, shelter, and food, and are emotionally affected. It is recommended that disaster management plans be developed to address all children's needs and nurses be provided with psychological support and training to improve their knowledge and skills.

Special Issue on The Resilience Research Council of Japan

We are glad that this special issue takes up the activities of The Resilience Research Council of Japan (RRCJ). Twelve years have passed since the founding of the RRCJ. In addition to regular meetings at which experts from various fields participate in disseminating information, one of the RRCJ’s main activities is holding study groups to delve deeply into specific aspects of disaster preparedness. To respond to the intensification and frequent occurrence of disasters in recent years, it is urgent to review existing measures against disasters and business continuity plans (BCPs). Furthermore, comprehensive and flexible strategies should be developed to enhance disaster resilience. Each study in this special issue provides new perspectives and clues on this problem. First, the existing BCPs lack concrete measures from the perspectives of individual corporate departments and employees. To tackle this problem, new procedures are proposed to formulate BCPs based on the questions “What if?” and “What when?” This is expected to increase the effectiveness of BCPs. Second and third, comprehensive methods for evaluating resilience are lacking. As a result of an examination of the 13 current indexes of “Resilient Organizations” and the “Organization Resilience Index” of the British Standards Institution, it is revealed that some important elements are absent from the evaluation of corporate resilience. By complementing the corporate resilience model with these elements, the model can be improved. Furthermore, local governments must implement effective disaster countermeasures for those with limited resources in their disaster management plans. The fourth study evaluates disaster management measures from the point of view of both local governments and residents, and proposes innovative solutions to collaborate and organize, adapt to the environment, and manage for the next generation. This reveals the problems faced by local governments and residents’ lack of knowledge, and provides a roadmap for future improvements. Overall, these studies indicate that creating a resilience strategy involving all stakeholders is an urgent matter, provide valuable insight into the limits of current strategies, and offer guidelines for strengthening the important aspects of resilience so that corporations and local governments can respond to disasters more effectively. We expect that the effectiveness of disaster measures will be enhanced through incorporating these proposals and methods into practical policies and actual works. We sincerely thank all the contributors for their contributions to this special issue and the reviewers for their valuable comments.

Study on the Prototype of Platform Tools and Practice Related to the “Sustainability Scoring Index” for Disaster Resilience of Local Governments in Japan

With the intensification and rising frequency of disasters in recent years, implementing disaster preparedness measures has become increasingly important. However, local governments must be prepared for disasters, with their limited resources of personnel and budget, to make the redressal plans relevant for facing problems such as population decline. This study aims to enhance resilience in the field of disaster management by facilitating identification of problems in the disaster management plan of local governments, and promotion of mutual understanding among those involved. For this purpose, a tool has been developed to evaluate local disaster management measures from the viewpoints of both local governments and residents, and to promote smooth communication and coordination among the stakeholders. A questionnaire survey was conducted using an index for self-assessment by local government officials, which was formulated based on the literature of the United Nations Office for Disaster Risk Reduction. Based on the survey results, the elements of resilience were classified into three groups depicting the abilities to collaborate and organize, adapt to the environment, and manage for the next generation. The evaluation was conducted in four local governments. In two regions, the residents’ awareness and preparedness for a disaster were surveyed. The survey results revealed that the questionnaire should be improved; nevertheless, the authors were able to identify the problems of local governments and the fields that the residents do not know sufficiently about. The results indicate the possibility to apply this method in other regions in the future.

Climate change adaptation and disaster risk reduction in South Africa’s local municipal plans

Climate change adaptation (CCA) and disaster risk reduction (DRR) agendas converge in their priorities to reduce social vulnerability and enhance resilience in the face of climate change. However, they are often treated separately in practice and in local government plans. In South Africa, while municipalities are legally mandated to develop Integrated Development Plans (IDPs) and Disaster Management Plans (DMPs), this is not the case for CCA. In this study, we assessed the engagement with, and linkages between, CCA and DRR in 20 local municipalities from contrasting socio-economic contexts in the Eastern Cape Province of South Africa by conducting an analysis of their IDPs, DMPs and any plans or strategies pertaining to CCA. Our results show that none of the municipalities had separate CCA strategies, with only two municipalities complying with the mandate to have DMPs. While municipalities mostly included CCA- and DRR-related activities in their IDPs, there were no explicit linkages made between the two approaches. Apparent was the prioritisation of bolstering infrastructure with little attention towards addressing social vulnerability and building resilience. Overall, little has been done to prioritise or link CCA and DRR at the local government level, especially in the context of addressing social vulnerability and building resilience. These findings suggest the need for South Africa to implement and promulgate legal instruments, policies and resources to capacitate local municipalities in mainstreaming and integrating CCA and DRR.

Increasing the Preparedness through Participatory Action Research in the Implementation of the Disaster Resilient Village Program in Madegondo Village

Climate change is increasing the frequency of floods in Indonesia, thereby triggering the need for effective disaster management down to detailed levels such as the Destana Program. Madegondo Village, Sukoharjo Regency experiences floods every year. It has become the focus of research to improve community preparedness using the Participatory Action Research (PAR) method with Focus Group Discussion (FGD) as the main technique. This research revealed that Madegondo Village is vulnerable to tornadoes, fires, and dengue fever. Risk analysis indicates a moderate level of danger in affecting human, economic, infrastructure, environmental, and socio-political assets. Furthermore, the creation of flood disaster risk maps, the Disaster Risk Reduction Forum, and disaster management plans were also carried out based on community participation. An early warning system was also developed via telephone and WhatsApp based on data from Kaliwingko and Bengawan Solo River.

Stormy sales: the influence of weather expectations on FMCG consumption

PurposeThis study aims to understand the impact of extreme weather events on fast-moving consumer goods (FMCG) consumption and to examine the role of anticipated product scarcity and FMCG types on such behavior.Design/methodology/approachThis paper conducted five studies, combining archival data analysis with behavioral experiments. The archival data included sales data from a supermarket chain and weather data from the National Weather Service. The experiments were designed to test the effect of extreme weather cues on consumption, the psychological mechanism behind this effect and moderators.FindingsThis research found that consumers’ anticipation of extreme weather events significantly increases their consumption of FMCGs. This research further discovered that these behaviors are driven by anticipated product scarcity and moderated by consumers’ altruisms and FMCG types.Research limitations/implicationsLimitations of the research include the reliance on reported sales data and self-reported measures, which could introduce biases. The authors also primarily focused on extreme weather events, leaving other types of disasters unexplored. Furthermore, cultural differences in disaster response might influence results, yet the studies do not fully address these nuances. Despite these limitations, the findings provide critical insights for FMCG retailers and policymakers, suggesting strategies for managing demand surges during disasters. Moreover, understanding consumer behavior under impending disasters could inform intervention strategies, potentially mitigating panic buying and helping ensure equitable resource distribution. Last, these findings encourage further exploration of environmental influences on consumer behavior.Practical implicationsThe findings have practical implications for products, brand managers and retailers in managing stock levels and product distribution during disasters. Furthermore, understanding the psychological mechanisms of these behaviors could inform policymakers’ designs of public interventions for equitable resource allocation during extreme weather events.Social implicationsThe research provides significant social implications by highlighting how extreme weather events impact FMCG consumption. This understanding can guide public policymakers in creating efficient disaster management plans. Specifically, anticipating surges in FMCG purchases can inform policies for maintaining price stability and preventing resource shortages, mitigating societal stress during crises. Moreover, these findings encourage public education around responsible purchasing during disasters, potentially reducing panic buying. By collaborating with FMCG manufacturers and retailers, governments can ensure a steady supply of essentials during extreme weather events. Thus, the research can play a crucial role in enhancing societal resilience in the face of impending disasters.Originality/valueTo the best of the authors’ knowledge, this is the first study to integrate the impact of extreme weather events on consumption behavior with the psychological theory of anticipated product scarcity. The unique focus on FMCGs offers a novel perspective on consumer behavior literature.

Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model

Climaate prediction plays a vital role in various sectors, including agriculture, disaster management, and urban planning. Traditional methods for climate forecasting often rely on complex physical models, which require substantial computational resources and may not accurately capture local weather patterns. This study explores the potential of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, for predicting daily climate variables such as temperature, precipitation, and humidity. Utilizing historical climate data from the city of Delhi, we developed an LSTM model to forecast short-term climate trends. The model consists of two LSTM layers followed by three Dense layers and is compiled with the Adam optimizer, mean squared error loss, and mean absolute error as a metric. Our results demonstrate the model's capability to capture temporal dependencies in climate data, achieving a satisfactory level of accuracy in temperature forecasting. This research underscores the potential of machine learning techniques, particularly LSTM networks, in enhancing climate prediction and contributing to more informed decision-making in weather-sensitive sectors.

Impact of information provision on tsunami evacuation behavior of residents and international tourists in Japan

Effective disaster management can help mitigate both human and financial damage. However, the absence of appropriate disaster preparedness and management efforts can increase the vulnerability of international tourists to natural disasters. International tourists differ from residents and local tourists in various ways, including limited knowledge of disasters and disaster responses, use of different languages, limited access to signs, and shelter locations and evacuation routes. This study developed an agent-based evacuation model to determine the differences in evacuation behaviors among international tourists, residents, and local tourists during a tsunami. Further, it clarified the impact of both soft and hard countermeasures such as information provision and shelter capacity expansion. Our case study was conducted in the Minato Bay area in Osaka, Japan. The simulation results revealed disparities in the evacuation behaviors between international tourists and residents in the arrival times at shelters. The enhanced sign accessibility and provision of signs in multiple languages significantly reduced the arrival time of international tourists at shelters. Consequently, it is necessary to improve disaster management plans that ensure information provision in multiple languages and the establishment of temporary shelters near tourism spots to support international tourists during disaster evacuation.