Improve Disaster Preparedness Research Articles

Membangun Generasi Tangguh Bencana Meningkatkan Wawasan Dan Sikap Siswa SMPN 1 Songgon Kabupaten Banyuwangi

Disaster mitigation education in the school environment is becoming increasingly vital in anticipating and responding to the threat of natural disasters. This research explores the impact of disaster mitigation training at SMPN 1 Songgon, Banyuwangi, conducted with the Regional Disaster Management Agency (BPBD) Banyuwangi, focusing on the concept of “Disaster Resilient Schools”. The community service method was to bring about changes in the awareness and preparedness of the school and community towards disasters. The results of the service showed that the training has made a significant contribution in improving the understanding of disaster risks and appropriate mitigation strategies. In addition, close collaboration between the school with UKM. Resima Stikes Banyuwangi and BPBD has been established, strengthening sustainable mitigation efforts. The training also encouraged active participation of students and teachers, and built a strong support network among various stakeholders. This activity not only provided new insights into the importance of disaster mitigation education in schools, but also provided practical guidance for the development of similar programs in other school settings. The implications of this service are very important in improving disaster preparedness and resilience at the local level, as well as contributing to a better understanding of effective disaster mitigation practices. The conclusion of this service is that it is very important and necessary to implement this activity in school environments throughout Banyuwangi district.

Exploring disaster preparedness in an obstetric unit in a district hospital in the Western Cape Province

BackgroundResearch on disaster preparedness in public hospitals is limited, and specialised units such as obstetric departments need to be even more prepared when rendering health care to vulnerable populations. Disasters can be natural, such as floods due to human interventions, sinkholes due to mining, or pandemic occurrences, such as the recent COVID-19 pandemic. Research on disaster preparedness is limited, and even more so in specialised units such as obstetrics and evacuating a ward of maternal and neonatal patients present unique challenges. Being prepared for any disaster is the only assurance of effective patient healthcare during a disaster. This study explored and described nurses’ knowledge and attitudes regarding preparedness for a disaster in an obstetric unit in a public institution. The study aimed to make recommendations to improve disaster preparedness in an obstetric ward based on the nurses’ knowledge and attitudes.MethodsThis study utilised an exploratory, descriptive qualitative design within a contextual approach. The data were acquired through individual interviews that were done using a semi-structured interview schedule. An observational walkabout was performed with the unit manager to validate interviewee responses. The study employed purposive sampling with a sample size of 17 nurses (N = 32, n = 17) and a response rate of 53%. The interviews were transcribed verbatim, and later, the data underwent analysis using theme analysis and a co-coder.ResultsThe results indicate that the participants demonstrate an awareness of disaster terminology but need more assertiveness in executing the institutional disaster policy. The results illustrate that more frequent training, disaster rehearsals, and simulations should be implemented to improve disaster readiness. Strategies are recommended to enhance preparedness for a disaster in the obstetric unit.ConclusionThe study findings recommend more education and training opportunities that should be regularly instilled as a practice within the obstetric ward. More disaster drills and simulation exercises should be performed to ensure confidence in disaster preparedness. Obstetric staff of all levels should be involved with policymaking and disaster plan development.

Designing and Psychometric Evaluation of a Questionnaire for Assessing Society's Perception of Flash Flood Risk

Background: Public perception of risks associated with natural disasters like flash floods significantly influences disaster management effectiveness. Flash floods pose a major threat to life and property, and a lack of tools to evaluate societal risk perception creates a critical gap. Objectives: This study aimed to design and evaluate the psychometric properties of a questionnaire specifically assessing society's perception of flash flood risk. Methods: A two-stage approach was employed. The first stage involved collecting questionnaire items through a systematic literature review and a qualitative study. In the second stage, a comprehensive psychometric evaluation was conducted, including assessments of face validity, content validity, and construct validity. Reliability was established using Cronbach's alpha. Confirmatory factor analysis was further conducted with data collected from 136 community members who had experienced flash floods. Data analysis was performed using SPSS version 20 and AMOS software. Results: A combination of qualitative data and systematic review findings facilitated the development of 36 initial questionnaire items. Applying quantitative and qualitative construct validity measures led to the refinement of the instrument, resulting in 29 final items categorized into three domains: Risk perception (12 items), awareness (10 items), and preparedness (7 items). The internal consistency of the instrument was confirmed by a Cronbach's alpha coefficient of 0.88. Factor analysis further supported the good fit of the hypothesized model to the data. Conclusions: This study successfully developed and rigorously evaluated a questionnaire to assess society's perception of flash flood risk. This instrument offers valuable insights for informed decision-making and identification of factors influencing risk perception, ultimately contributing to improved disaster preparedness and management.

Smart Health Village in Improving Disaster Preparedness

Background: Disaster-prone areas are areas that have the potential for natural disasters such as earthquakes, mountain eruptions, landslides caused by activity or movement from the base of the earth. Understanding the potential for natural disasters in each region as one of the anticipatory steps to minimize the number of victims of natural disasters. Precise and accurate information is needed for disaster management. The slope of the area is quite steep, and road access to villages and tourist attractions is vulnerable to landslides. The current disaster incident information reporting system still applies the conventional model. The community also experiences difficulties in receiving information and submitting reports regarding village conditions. Purpose: This research aims to analyze the effectiveness of smart health villages in improving disaster preparedness. Method: The research design used a pre-experiment with a pre-test and post-test design approach with a sample size of 64 volunteers who were taken using purposive sampling. The independent variable in this study is Smart Health Village and the dependent variable is the voluntary task force's ability to preparedness disaster. The research instrument used a questionnaire for each variable. Data analysis in this study is the Wilcoxon Signed Rank Test. The implementation of this activity consists of the design stage, application creation, system design, socialization and training, and trial. Results: The research results showed that the voluntary task force's preparedness was in a good category as much as 88% and as enough as 12% with p value = 0.000. This application system smart health village based on the web allows both the community (users), admin (task force), and verifiers (village officials) to access anywhere and at any time, thus increasing the capability and quality of human resources in disaster preparedness and disaster-prone areas. Conclusion: Based on the results it can be concluded that smart health village can improve ability in preparedness of disaster in disaster- prone areas thus reducing impact of anxiety and and panic due to disaster.

Breakthroughs in satellite remote sensing of floods

Flooding is among the top-ranking disasters worldwide, evident through frequent and devastating events causing significant localized impacts and broader repercussions. Floods lead to substantial insured and uninsured losses, with a few hundred billions of $US in flood-related losses over the last 5 years, only a moderate amount of which were insured. Remote sensing, especially via satellite technology, has great potential for flood mapping and monitoring. Although many initiatives utilize satellites for flood response, few have resulted in operational protocols for mandated response organizations. Historic breakthroughs in satellite remote sensing have occurred since the 1970s, with six major milestones enhancing flood monitoring over the last half century. This article looks back at these technological development breakthroughs and the barriers to progress they lifted. Advancements in machine learning, cloud computing, and increased satellite missions promise more developments. Anticipated innovations include satellite constellations with various sensors and self-learning processing models to relay real-time insights for disaster response. Looking forward, a transformative shift in flood mapping from space may be expected as early as 2025, driven by enhanced orbital computing for predictive capabilities, improving disaster preparedness and response.

Sentimen Analysis Social Media for Disaster using Naïve Bayes and IndoBERT

The rapid advancement of information and communication technology has resulted in a significant surge in data, especially text data from social media platforms. This paper presents a sentiment analysis approach using IndoBERT and Naïve Bayes algorithms to classify sentiment related to natural disasters, specifically from a dataset of tweets derived from social media platform X. The focus of this research is to categorize tweets as positive and negative sentiment to provide useful insights in improving disaster response and management, with a focus on tweets related to earthquakes, floods, and the eruption of Mount Merapi. The goal is to assist the government in allocating aid more efficiently and understanding public sentiment during disasters. The methodology used includes data collection, data preparation, labeling, categorization, word weighting using tf-idf, data separation, and classification using Naïve Bayes and IndoBERT algorithms. The results showed that IndoBERT achieved 91% accuracy, while Naïve Bayes achieved 74% accuracy. The study highlights the potential of sentiment analysis in improving disaster preparedness and more effective response strategies.

Pelatihan Kesiapsiagaan Bencana Gempa Bumi pada Guru SMP di Kota Masohi Kabupaten Maluku Tengah

Maluku Province, including Central Maluku, is one of the areas where earthquakes occur most frequently and is at risk of causing problems in people's lives. To overcome the impacts, efforts need to be made to minimize losses due to earthquakes, namely by providing information about disaster preparedness through public facilities such as schools. This is supported by the participation of school stakeholders, one of which is teachers. This community service aims to increase the earthquake preparedness of SMPN teachers in Masohi City. This service activity was carried out through earthquake disaster preparedness training for 60 teachers from 3 SMPN schools in Masohi City, which included providing education, simulation mentoring, and evaluation. The results of this training were proven to be able to improve disaster preparedness for SMPN teachers in Masohi City as evidenced by the increase in the disaster knowledge score (KAP) from 69 to 82; emergency response plan (EP) from 75 to 85; disaster warning (WS) from 60 to 79; resource mobilization (RMC) from 57.4 to 76. Through this training, it is hoped that teachers, especially class teachers, can inform and train students and other teachers in disaster preparedness, especially earthquakes.

Utilizing the HEC-HMS Model for Hydrological Modelling of the Rambiara Watershed: Rainfall and Runoff Estimation

Abstract: Hydrological modelling and flood prediction are essential components of the Rambiara watershed's water resource management. To improve disaster preparedness and reduce the danger of flooding in the area, the effectiveness of the HEC HMS model in flood forecasting must be assessed. In this study, various techniques for estimating rainfall are examined, and a customized hydrological model tailored to the hydrological complexities of the Rambiara watershed is developed. The goal of this project is to provide practical insights for better flood risk reduction and water resource management through the integration of cutting-edge modelling tools and rigorous analysis. This work advances our knowledge of watershed dynamics and provides guidance for sustainable water resource planning projects in the Rambiara watershed and beyond by thoroughly examining precipitation patterns and hydrological processes. In addition, the research takes into account a number of factors related to flood behaviour, such as determining places that are vulnerable to flooding, analysing the size of floods, and estimating how frequently they occur. By taking a holistic approach, it is possible to gain a thorough understanding of the dynamics of flooding in the Rambiara watershed, which paves the way for the development of efficient flood risk management techniques. This study looks into the temporal and spatial variability of rainfall in the Rambiara watershed in addition to research on floods. Through the comparison of several methods for estimating rainfall, including deterministic and geostatistical approaches, the study offers valuable insights into the precision and dependability of rainfall data, which are essential for hydrological modelling and flood forecasting. The hydrological processes of the Rambiara watershed can be more accurately represented by creating a bespoke hydrological model with the HEC HMS program. Informed decision-making and proactive flood risk reduction strategies are made possible by the study's model calibration and validation, which guarantee the model's dependability in simulating runoff and forecasting flood events. Overall, this study advances our knowledge of flood dynamics in the Rambiara watershed and advances hydrological modelling tools. The goal of this study is to promote sustainability and resilience in the face of hydrological hazards by offering practical insights for managing water resources and reducing the danger of flooding.

Understanding flash flooding in the Himalayan Region: a case study

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

A Mutual Catastrophe Insurance Framework for Horizontal Collaboration in Prepositioning Strategic Reserves

Improving Disaster Preparedness Through Mutual Catastrophe Insurance In “A Mutual Catastrophe Insurance Framework for Horizontal Collaboration in Prepositioning Strategic Reserves,” H. Zbib, B. Balcik, M.-È. Rancourt, and G. Laporte present an innovative approach to collaborative disaster preparedness. The novel framework considers a risk-averse mutual insurer offering multiyear insurance contracts with coverage deductibles and limits to a portfolio of risk-averse policyholders. It is designed to foster horizontal collaboration among policyholders for joint disaster preparedness by effectively integrating operational and financial functions. The problem is modeled as a large-scale nonlinear multistage stochastic program and solved by using an effective Benders decomposition algorithm. The framework is validated with real data from 18 Caribbean countries focusing on hurricane preparedness. Given the predicted impacts of climate change, the proposed multiyear mutual catastrophe insurance framework promises to reshape global disaster preparedness and make a profound societal impact by providing a transparent disaster financing plan to protect vulnerable regions. The study’s findings stress the importance of long-term cooperation, prenegotiation of indemnification policies, and strategic setting of deductibles and limits by taking into account the correlation between policyholders.

Advanced hybrid CNN-Bi-LSTM model augmented with GA and FFO for enhanced cyclone intensity forecasting

Predicting cyclone intensity is an important aspect of weather forecasting since it influences disaster preparation and response. This framework addresses the pressing need for precise cyclone intensity prediction by presenting a unique predictive model based on a hybrid CNN and Bi-LSTM architecture optimized using a Genetic Algorithm (GA) enhanced Fruit Fly Optimizer (FFO). Existing methods have primarily relied on traditional machine learning models and meteorological data, demonstrating limitations in capturing the complex spatial-temporal patterns inherent in cyclone evolution. These drawbacks include insufficient feature extraction abilities, underutilization of convolutional neural networks (CNN), and poor model tuning. This unique method incorporates a hybrid CNN and Bi-LSTM architecture that is tuned by a Genetic Algorithm (GA) enhanced Fruit Fly Optimizer (FFO), resulting in higher cyclone intensity prediction accuracy. The experimental results are implemented in Python software, and they reveal that this method outperforms current models by an average of 21% when compared to existing methods such as VGG-16 achieved an accuracy of 78% and Ty 5-CNN (95.23%). The suggested CNN-Bi-LSTM model predicts cyclone strength with an excellent accuracy of 99.4%. This unique approach offers a possible avenue for increasing cyclone intensity prediction, hence improving disaster preparedness and risk mitigation efforts in sensitive locations.

Young people's experiences of the Northern Rivers 2017 flood and its effects on their mental health.

To explore young people's (YP) experiences of catastrophic flooding in the Northern Rivers area of northern NSW in 2017 and its effect on their mental health. Content analysis of free-text survey responses (written responses to open questions, rather than selecting a response option). Northern Rivers NSW. YP aged 16-25 years who were Northern Rivers residents at the time of the 2017 flood. YP found their flood experience to be novel and challenging. They expressed worry, distress, anxiety, and worsened pre-existing mental health issues as a result of the flood. YP reported a number of primary stressors (inadequate preparedness, warning and support during the flood) and secondary stressors (loss/damage of property and possessions, financial strain and disruptions to daily life) which potentially contributed to poor mental health. Some YP reported positive outcomes from their flood experience, notably increased community connectedness and personal resilience. However, they also expressed concern for the future, particularly potential reoccurrence of extreme floods as well as climate change. YP conveyed a desire for better community involvement to improve preparedness for future floods and a clear motivation to take action on climate change. The flood was a challenging experience for YP which was commonly described as negatively affecting their mental health. Understanding what YP view as significant events or issues arising from their personal flood experience may help target support mechanisms and services to maintain their mental. More focus on community-based initiatives to improve disaster preparedness can support mental health in YP.

Inequities in the Impacts of Hurricanes and Other Extreme Weather Events for Cancer Survivors.

In this minireview, we examine the impacts of hurricanes and other extreme weather events on cancer survivors, focusing on structural and social determinants of health. We briefly explore influences on biological, psychosocial, and behavioral outcomes and discuss risk and resilience factors in cancer survivorship during and after hurricanes. Our goal is to inform future directions for research that can identify areas in which we can most efficiently improve cancer outcomes and inform changes in health systems, clinical practice, and public health policies. This timely minireview provides researchers and clinicians with an overview of challenges and opportunities for improving disaster preparedness and response for cancer survivors.

An integration of geospatial and fuzzy-logic techniques for multi-hazard mapping

A hazard is a natural occurrence that might harm humans, animals or the environment. It may cause loss of life, illness or other health consequences, property damage, social and economic crisis or environmental degradation. Many places of the world are at risk from one or more disasters. Although many studies have concentrated on single hazards, there is a need for integrated evaluations of multi-hazards for more effective land management. A selection of datasets and methods, such as meteorological data, satellite images, and GIS, were used to create the risk assessment maps. The parameters for multi-hazard assessment are mainly considered as rainfall, slope, elevation, and land use/land cover and create a map in a GIS environment. For a particular region, multi-hazards can be produced by integrating maps of several hazard assessments. The objective of this study is an integration of geospatial and fuzzy-logic techniques for multi-hazard mapping. Extensive parts of Gujarat state (India) experience a wide range of natural hazards: floods, soil erosion, drought, and earthquakes. This research creates and evaluates individual and group multi-hazard maps to visualize the spatial variation of hazards in Gujarat state, India. The calculated four individual hazard maps have been categorised into five classes: very-low, low, moderate, high, and very high. The multi hazard map has been classified into sixteen classes using the GIS unsupervised. This study aims to improve disaster preparedness, enhance land management, or guide decision-making for disaster risk reduction. This study can be helpful in the future to engineers, planners, and local governments in the field of spatial planning and natural disaster management.

Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery

ABSTRACT This study introduces a cost-effective and automated methodology to swiftly and accurately gauge disaster-induced land cover changes, harnessing satellite imagery and machine learning. Using open-source platforms like Google Earth Engine, the approach streamlines training sample generation and assesses diverse machine learning algorithms on pre- and post-disaster Landsat images. The innovation lies in its semi-automatic training sample generation methodology, reducing time and manual effort for accurate land cover mapping and addressing the challenge of crafting training samples in disaster settings. The methodology was tested on the 2010 Chilean earthquake and tsunami, revealing a 45% increase in sediment cover and a 15% reduction in water cover, underlining vast landscape alterations. SVM stood out for its consistent accuracy. To validate the methodological robustness and broaden its applicability, we replicated the procedures using diverse land cover collection, including ESA WorldCover 10 m and Dynamic World. This comprehensive validation encompassed varied catastrophic events, extending the methodology’s relevance beyond its original scope. The consistent findings across different scenarios underscored the method’s robustness and underscored its potential for broader utilization. This streamlined, cost-effective approach suits multiple land cover products, setting the stage for constant Earth feature monitoring. In light of escalating natural disasters, the synergy of machine learning and geospatial technology spotlighted here offers promising avenues for improved disaster preparedness and response.

Dynamic bivariate hazard forecasting of hurricanes for improved disaster preparedness

Hurricanes associated with strong winds and heavy rainfall, significantly impact lives and property globally. Traditional approaches focusing only on wind speed lack a comprehensive assessment of potential impacts. Here, we present an innovative method for generating real-time dynamic bivariate hazard assessments for hurricanes, with a specific focus on wind and rainfall, offering a holistic perspective on probable impact. Demonstrated on four hurricanes, this method computes the probabilistic wind and rainfall hazard values at different lead days. These are categorized and presented as bivariate hazard maps to facilitate straightforward interpretation and efficient communication. These maps provide insights into the combined hazard and highlight the individual contributions of wind and rainfall across different lead times, empowering stakeholders to strategize preparedness and precautionary measures. This method provides a novel generic approach for communicating forecast through dynamic bivariate hazard maps, applicable to a range of extreme events such as floods, wildfires, and droughts.

Lessons from COVID-19 for GCR governance: a research agenda.

The Lessons from Covid-19 Research Agenda offers a structure to study the COVID-19 pandemic and the pandemic response from a Global Catastrophic Risk (GCR) perspective. The agenda sets out the aims of our study, which is to investigate the key decisions and actions (or failures to decide or to act) that significantly altered the course of the pandemic, with the aim of improving disaster preparedness and response in the future. It also asks how we can transfer these lessons to other areas of (potential) global catastrophic risk management such as extreme climate change, radical loss of biodiversity and the governance of extreme risks posed by new technologies. Our study aims to identify key moments- 'inflection points'- that significantly shaped the catastrophic trajectory of COVID-19. To that end this Research Agenda has identified four broad clusters where such inflection points are likely to exist: pandemic preparedness, early action, vaccines and non-pharmaceutical interventions. The aim is to drill down into each of these clusters to ascertain whether and how the course of the pandemic might have gone differently, both at the national and the global level, using counterfactual analysis. Four aspects are used to assess candidate inflection points within each cluster: 1. the information available at the time; 2. the decision-making processes used; 3. the capacity and ability to implement different courses of action, and 4. the communication of information and decisions to different publics. The Research Agenda identifies crucial questions in each cluster for all four aspects that should enable the identification of the key lessons from COVID-19 and the pandemic response.

Understanding Disaster Literacy Level in Indonesia: How can students understand natural disasters?

This study aims to determine the understanding of disaster literacy in preparing disaster-resilient students. This study used a quantitative descriptive method with a quasi-experimental design. The type of research used in this research is one group pretest-posttest. The research population is students of a public junior high school in Lembang, grade 7, for the odd semester of the 2022/2023 academic year. The research sample consisted of 33 students in class 7A. Data was collected through tests and documentation for two months, from November to December 2022. Then, the data were analyzed using descriptive statistics. Data analysis used paired t-test with the result that there was a significant difference between the pretest and posttest scores of 33 students with p 0.000. Based on these results, disaster literacy is very effective in improving disaster preparedness, especially in preparing aspects of disaster literacy knowledge, attitudes, and skills. The recommendations in this study are that only a few researchers apply disaster literacy in schools so that disaster literacy activities can become routine habits carried out by students and schools.

The Umlindi Newsletter: Disseminating Climate-Related Information on the Management of Natural Disaster and Agricultural Production in South Africa

The Umlindi newsletter was developed to provide information towards climate advisories, considering, for instance, drought conditions, presented in a relevant manner for the agricultural and disaster sectors in South Africa. This newsletter, which is disseminated on a monthly basis, provides information derived from climate-related monitoring products obtained from an integration of remote sensing and in situ data from weather stations. It contains useful indicators, such as rainfall, vegetation, and fire conditions, that provide an overview of conditions across the country. The present study demonstrates how these natural resource indices are integrated and consolidated for utilization by farmers, policy-makers, private organizations, and the general public to make day-to-day decisions on the management and mitigation of natural disasters. However, there is a need to expand these baseline observation initiatives, including the following: (1) forecasting future conditions to strengthen coping mechanisms of government, farmers, and communities at large; and (2) incorporating information on other natural disasters such as floods and extreme heat. In the context of South Africa, this information is important to improve disaster preparedness and management for agricultural productivity. In a global context, the Umlindi newsletter can be insightful for developing and disseminating natural resources information on adaptation to and mitigation of climate change and variability impacts to other regions facing similar risks. Furthermore, while international organizations also provide natural resource information, the Umlindi newsletter may be distinguished by its regional focus and linkages to individual communities. It bridges the gap between global environmental data and local decision-making by illustrating how global scientific knowledge may be applied locally.

Localization of seismic waves with submarine fiber optics using polarization-only measurements

Monitoring seismic activity on the ocean floor is a critical yet challenging task, largely due to the difficulties of physical deployment and maintenance of sensors in these remote areas. Optical fiber sensing techniques are well-suited for this task, given the presence of existing transoceanic telecommunication cables. However, current techniques capable of interrogating the entire length of transoceanic fibers are either incompatible with conventional telecommunication lasers or are limited in their ability to identify the position of the seismic wave. In this work, we propose and demonstrate a method to measure and localize seismic waves in transoceanic cables using only conventional polarization optics, by launching pulses of changing polarization. We demonstrate our technique by measuring and localizing seismic waves from a magnitude Mw 6.0 earthquake (Guerrero, Mexico) using a submarine cable connecting Los Angeles, California and Valparaiso, Chile. Our approach introduces a cost-effective and practical solution that can potentially increase the density of geophysical measurements in hard-to-reach regions, improving disaster preparedness and response, with minimal additional demands on existing infrastructure.