Warning System Research Articles

Evolution and genetic characterization of Seoul virus in wild rats Rattus norvegicus from an urban park in Lyon, France 2020-2022.

Seoul virus (SEOV) is an orthohantavirus primarily carried by rats. In humans, it may cause hemorrhagic fever with renal syndrome (HFRS). Its incidence is likely underestimated and given the expansion of urban areas, a better knowledge of SEOV circulation in rat populations is called for. Beyond the need to improve human case detection, we need to deepen our comprehension of the ecological, epidemiological, and evolutionary processes involved in the transmission of SEOV. We performed a comprehensive serological and molecular characterization of SEOV in Rattus norvegicus in a popular urban park within a large city (Lyon, France) to provide essential information to design surveillance strategies regarding SEOV. We sampled rats within the urban park of 'La Tête d'Or' in Lyon city from 2020 to 2022. We combined rat population genetics, immunofluorescence assays, SEOV high-throughput sequencing (S, M, and L segments), and phylogenetic analyses. We found low structuring of wild rat populations within Lyon city. Only one sampling site within the park (building created in 2021) showed high genetic differentiation and deserves further attention. We confirmed the circulation of SEOV in rats from the park with high seroprevalence (17.2%) and high genetic similarity with the strain previously described in 2011 in Lyon city. This study confirms the continuous circulation of SEOV in a popular urban park where the risk for SEOV transmission to humans is present. Implementing a surveillance of this virus could provide an efficient early warning system and help prepare risk-based interventions. As we reveal high gene flow between rat populations from the park and the rest of the city, we advocate for SEOV surveillance to be conducted at the scale of the entire city.

Nurse by numbers: The impact of early warning systems on nurses' higher-order thinking, a quantitative study.

To evaluate registered nurses' perceptions of whether the mandated use of the early warning system vital signs tool impacts the development of nurses' higher-order thinking skills. A concurrent mixed methods study design. Using an online survey, registered nurses' perceptions were elucidated on whether early warning system algorithmic tools affected the development of their higher-order thinking. Likert-type matrix questions with additional qualitative fields were used to obtain information on nurse's perceptions of the tool's usefulness, clinical confidence in using the tool, compliance with escalation protocols, work environment and perceived compliance barriers. Most of the 305 (91%) participants included in the analysis had more than 5 years of nursing experience. Most nurses supported the early warning tool and were happy to comply with escalation protocols if the early warning score concurred with their assessment of the patient (63.6%). When the score and the nurse's higher-order thinking did not align, some had the confidence to override the escalation protocol (40.0%), while others omitted (69.4%) or inaccurately documented vital signs (63.3%) to achieve the desired score. Very few nurses (3.6%) believe using early warning tools did not impede the development of higher-order thinking. Although experienced nurses appreciate the support of early warning tools, most value patient safety above the tools and rely on their higher-order thinking. The sustained development and use of nurses' higher-order thinking should be encouraged, possibly by adding a critical thinking criterion to existing algorithmic tools. The study has implications for all nurses who utilize algorithmic tools, such as early warning systems, in their practice. Relying heavily on algorithmic tools risks impeding the development of higher-order thinking. Most experienced nurses prioritize their higher-order thinking in decision-making but believe early warning tools can impede higher-order thinking. Registered nurses participated as survey respondents.

Macroeconomic Currency Crisis Early Warning Indicators in Emerging Countries

This study investigates the relationships between exchange rate and the main macroeconomic variables as GDP, inflation and unemployment on one hand and the ability of these variables in alerting about coming exchange rate crisis in emerging countries. The three variables have significant coefficients with exchange rate in line with literature signs except unemployment rate. The study uses signal approach, dealing specifically with the main macroeconomic variables, selected by system GMM method in emerging markets. The study develops macroeconomic pressure indices from these selected macroeconomic variables using the market pressure index methodology from Early Warning System literature. Based on the macroeconomic variables, a combined macroeconomic pressure index has been built. The results of the non-parametric early warning system indicate that the individual macroeconomic pressure indexes created are good warning tools of a currency crisis. The macroeconomic pressure indexes are better early warning indicators than market pressure index built from international reserves, in emerging countries for four quarters warning period window. Production pressure index appears more accurate followed by inflation but unemployment pressure index is the most sensitive. However, the number of effective indicators and the accuracy of the indexes are not the same for all the countries, changing from a country to another.

Affordable Sensors for Speditive and Accurate Documentation of Built Heritage: First Tests and Preliminary Results

Abstract. When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple’s iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint.

Targeted Formation of Biofilms on the Surface of Graphite Electrodes as an Effective Approach to the Development of Biosensors for Early Warning Systems

Biofilms based on bacteria Pseudomonas veronii (Ps. veronii) and Escherichia coli (E. coli) and yeast Saccharomyces cerevisiae (S. cerevisiae) were used for novel biosensor creation for rapid biochemical oxygen demand (BOD) monitoring. Based on the electrochemical measurement results, it was shown that the endogenous mediator in the matrix of E. coli and Ps. veronii biofilms and ferrocene form a two-mediator system that improves electron transport in the system. Biofilms based on Ps. veronii and E. coli had a high biotechnological potential for BOD assessment; bioreceptors based on such biofilms had high sensitivity (the lower limits of detectable BOD5 concentrations were 0.61 (Ps. veronii) and 0.87 (E. coli) mg/dm3) and high efficiency of analysis (a measurement time 5–10 min). The maximum biosensor response based on bacterial biofilms has been observed in the pH range of 6.6–7.2. The greatest protective effect was found for biofilms based on E. coli, which has high long-term stability (151 days for Ps. veronii and 163 days for E. coli). The results of the BOD5 analysis of water samples obtained using the developed biosensors had a high correlation with the results of the standard 5-day method (R2 = 0.9820, number of tested samples is 10 for Ps. veronii, and R2 = 0.9862, number of tested samples is 10 for E. coli). Thus, biosensors based on Ps. veronii biofilms and E. coli biofilms could be a novel analytical system to give early warnings of pollution.

Learning Material Organization for Disaster Resilient Villages

This study aims to express the best learning material organization for the Disaster Resilient Village Program (Program Desa Tangguh Bencana, DESTANA). It was delivered to the people of Pacitan Regency as a disaster-prone area. Various potential disasters exist in the area of Pacitan Regency. The learning material is organized in line with the revised Bloom’s Taxonomy, which applies six levels of thought processes. The learning process starts from a hierarchical structure that identifies lower-level thinking skills to higher levels of skills that are ready for operational implementation. The Lower Order Thinking Skills materials encompass understanding various disaster vulnerabilities based on geographical conditions, the nature of disaster threats and risks, participatory disaster management by the community, principles of evacuation planning, the role of Disaster Management Plans, the paradigm of disaster risk management, principles of the Early Warning System (EWS), the purpose of contingency plans, and principles of first aid in emergencies. The Higher Order Thinking Skills materials include the ability to measure indicators for disaster-resilient villages, establish volunteer working groups, design evacuation stages, develop Disaster Management Plans, Disaster Risk Management, Early Warning Systems (EWS), and Village Contingency Plans. The learning achievement is measured through community understanding and practice, the success of building a disaster response coordination structure, and activities in a disaster simulation. It is organized in seven discussion forums. The program resulted in significant achievements. The Pacitan Regency Government implemented the Disaster Resilient Village Program for the safety of its residents. Keywords: disaster, resilient village, learning material

Deformation Analysis and Prediction of a High-Speed Railway Suspension Bridge under Multi-Load Coupling

High-speed railway suspension bridges (HSRSBs) have been constructed with the new advancements in technology. The deformation prediction for HSRSBs is essential to their safety and maintenance. The conventional prediction methods are developed for bridges without high-speed railway. Different factors, including temperature (TEMP), time delay compensation (TDC), train live load (TLL), are considered in these methods. However, the train side (TS) and train instantaneous position (TIP) have a significant impact on deformation for HSRSBs, and they are not used in the prediction. More importantly, the coupling issue among different factors is so significant that it cannot be neglected. In this study, we propose a deformation prediction model based on a backpropagation (BP) neural network. This model uses different factors as model input, including TEMP, TDC, TLL, TS, and TIP. The coupling issue is addressed by using the new model. The new model was evaluated using a dataset of 10-day field measurements. It achieves a mean absolute error (MAE) of 8.81 mm, a mean relative error (MRE) of 9.82%, and coefficient of determination (R2) of 0.94. The new model will provide high-precision prediction for deformation and will be used in the development of an early warning system.

Evaluation of the single-frequency variometric approach based on low-cost GNSS observations and different satellite combinations for detecting short-term dynamic behaviors

Abstract This study presents the capability of the single-frequency (SF) variometric approach (VA) technique with low-cost GNSS observations to detect short-term dynamic behaviors. Harmonic oscillations with amplitudes between 5 and 20 mm and frequencies between 0.3 and 5.0 Hz were generated employing a single-axis shake table to investigate the performance of the SF-VA technique in the Structural Health Monitoring (SHM) system. Besides, a Mw 6.9 Kobe, 1995 earthquake simulation was generated using the shake table to analyze the SF-VA performance for the Earthquake Early Warning (EEW) system. A low-cost u-blox ZED-F9P GNSS receiver and ANN-MB-00 patch antenna were used to collect GNSS observations at a 20 Hz sampling rate during the experiments. The observations were processed using the MATLAB-based open-source PPPH-VA software in real-time mode, considering eight different satellite combinations. The capability of the SF-VA technique to detect horizontal dynamic behaviors in real-time mode was investigated in the frequency and time domains, accepting the displacements from the Linear Variable Differential Transformer (LVDT) sensor as a reference. The results in the frequency domain demonstrate that the SF-VA technique with low-cost GNSS observations can successfully detect the peak frequency value of short-term harmonic oscillations up to 5 Hz. Moreover, time domain findings emphasize that the short-time dynamic oscillations can be determined with the SF-VA technique with an accuracy ranging from 0.8 to 6.4 mm. Earthquake simulation experiment results demonstrate that the strong ground motions caused by mega earthquakes can be determined at mm-level by the SF-VA method. The results of both experiments show that multi-GNSS observations contribute to the SF-VA technique considerably. Overall, the findings reveal that the SHM and EEW systems can be operated with low-cost GNSS receivers, and the natural frequency of the man-made structures and accurate displacement values of seismic waveforms can be determined in real-time with the SF-VA technique.

Seismic and radon signatures: A multiparametric approach to monitor surface dynamics of a hazardous 2021 rock–ice avalanche, Chamoli Himalaya

AbstractThe observation of precursory signals of the 2021 Chamoli rock–ice avalanche provides an opportunity to investigate the multidisciplinary analysis approach of rock failure. On 7 February 2021, a huge rock–ice mass detached from the Raunthi peak at Chamoli district in Uttarakhand, India. The tragic catastrophe resulted in more than 200 deaths and significant economic losses. Here, we analyse radon concentration and seismic signals to characterise the potential precursory anomalies prior to the detachment. Continuous peaks of radon anomalies were observed from the afternoon of 5 to 7 February and decreased suddenly after the event, while a cumulative number of seismic tremors and amplitude variations are more intensified ~2.30 h before the main event, indicating a static to dynamic phase change within the weak zone. This study not only characterises abnormal signals but also models the rock failure mechanisms. The analysis unveils three time‐dependent nucleation phases, physical mechanisms of signal generation and a complete scenario of physical factors that affected the degree of criticality of slope failure. The results of this study suggest gradual progression of rock cracks/joints, subsequent material creep and slip advancement acceleration preceded the final failure. Furthermore, the study highlights the importance of an early warning system to mitigate the impact of events like the 2021 Chamoli rock–ice avalanche.

GRAPES: Earthquake Early Warning by Passing Seismic Vectors Through the Grapevine

AbstractEstimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield‐based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake.

Development of an early warning system for higher education institutions by predicting first‐year student academic performance

AbstractIn this study, an early warning system predicting first‐year undergraduate student academic performance is developed for higher education institutions. The significant factors that affect first‐year student success are derived and discussed such that they can be used for policy developments by related bodies. The dataset used in experimental analyses includes 11,698 freshman students' data. The problem is constructed as classification models predicting whether a student will be successful or unsuccessful at the end of the first year. A total of 69 input variables are utilized in the models. Naive Bayes, decision tree and random forest algorithms are compared over model prediction performances. Random forest models outperformed others and reached 90.2% accuracy. Findings show that the models including the fall semester CGPA variable performed dramatically better. Moreover, the student's programme name and university placement exam score are identified as the other most significant variables. A critical discussion based on the findings is provided. The developed model may be used as an early warning system, such that necessary actions can be taken after the second week of the spring semester for students predicted to be unsuccessful to increase their success and prevent attrition.

Performance of a machine learning-based early warning system in hospitalized patients with diabetes mellitus

Performance of a machine learning-based early warning system in hospitalized patients with diabetes mellitus

Measuring the state of aquatic environments using eDNA-upscaling spatial resolution of biotic indices.

Aquatic macroinvertebrates, including many aquatic insect orders, are a diverse and ecologically relevant organismal group yet they are strongly affected by anthropogenic activities. As many of these taxa are highly sensitive to environmental change, they offer a particularly good early warning system for human-induced change, thus leading to their intense monitoring. In aquatic ecosystems there is a plethora of biotic monitoring or biomonitoring approaches, with more than 300 assessment methods reported for freshwater taxa alone. Ultimately, monitoring of aquatic macroinvertebrates is used to calculate ecological indices describing the state of aquatic systems. Many of the methods and indices used are not only hard to compare, but especially difficult to scale in time and space. Novel DNA-based approaches to measure the state and change of aquatic environments now offer unprecedented opportunities, also for possible integration towards commonly applicable indices. Here, we first give a perspective on DNA-based approaches in the monitoring of aquatic organisms, with a focus on aquatic insects, and how to move beyond traditional point-based biotic indices. Second, we demonstrate a proof-of-concept for spatially upscaling ecological indices based on environmental DNA, demonstrating how integration of these novel molecular approaches with hydrological models allows an accurate evaluation at the catchment scale. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Environmental data in epidemic forecasting: Insights from predictive analytics

Epidemic forecasting plays a critical role in public health preparedness and response, enabling proactive measures to mitigate the impact of infectious diseases. Environmental data, encompassing factors such as temperature, humidity, air quality, and geographical features, holds valuable insights for predicting and identifying areas prone to epidemics. This paper explores the integration of predictive analytics with environmental data to enhance epidemic forecasting capabilities. By leveraging predictive analytics techniques, researchers and public health officials can analyze environmental data to identify regions at higher risk of experiencing epidemic outbreaks. Through statistical modeling, machine learning algorithms, and computational simulations, predictive analytics utilize environmental indicators to forecast the likelihood and spread of diseases. For example, areas with high temperatures and humidity may be conducive to mosquito-borne diseases, while regions with poor air quality may experience increased rates of respiratory infections. Case studies highlight the application of predictive analytics in various contexts, including forecasting mosquito-borne diseases in tropical regions and tracking respiratory infections in urban areas with poor air quality. Early warning systems, informed by environmental data, provide timely alerts to potential epidemic threats, enabling proactive interventions and resource allocation. While the integration of environmental data into epidemic forecasting offers significant benefits, challenges remain, including data quality, availability, and ethical considerations. Continued research and collaboration are essential to address these challenges and further enhance the effectiveness of predictive analytics in identifying and mitigating epidemic risks. In conclusion, this paper underscores the importance of leveraging environmental data and predictive analytics for epidemic forecasting, emphasizing their potential to improve public health outcomes and enhance preparedness efforts in the face of emerging infectious diseases and climate change. Keywords: Environmental Data, Epidemic Forecasting, Predictive Analytics.

Flood early warning system at Jakarta dam using internet of things (IoT)-based real-time fishbone method to support industrial revolution 4.0

This research aims to develop an effective flood early warning system to provide timely information to the public and support the government in disaster management. The Raspberry Pi mini-computer functions as the central control, collecting data from the Water Level Sensor to measure water height, the Ultrasonic Sensor for further monitoring, the DHT11 Sensor to monitor temperature and humidity, and a Buzzer as an audible warning device. The research method involves review of the literature and data acquisition from related journals. These data are utilized to design an Internet of Things (IoT)-based flood detection tool with the Raspberry Pi minicomputer as the main controller. The system can be implemented in vulnerable locations such as reservoirs, sluice gates, and rivers, as part of the Smart City and Smart Environment concepts. The test results indicate that the developed early warning system, integrating the Raspberry Pi minicomputer, the Water Level Sensor, the the Ultrasonic Sensor DHT11 Sensor, and Buzzer, approaches perfection. Real-time information is transmitted through the Twitter social media platform, which is shown to be more effective than manual notifications. The system can provide accurate early warnings, reduce flood-related damages, and positively contribute to flood prevention and disaster management efforts. This research is expected to make a significant contribution to improving the community and government preparedness for future flood disasters.

Four Storm Surge Cases on the Coast of São Paulo, Brazil: Weather Analyses and High-Resolution Forecasts

The coast of São Paulo, Brazil, is exposed to storm surges that can cause damage and floods. These storm surges are produced by slowly traveling cyclone–anticyclone systems. The motivation behind this work was the need to evaluate high-resolution forecasts of the mean sea-level pressure and 10 m winds, which are the major drivers of the wave model. This work is part of the activity in devising an early warning system for São Paulo coastal storm surges. For the evaluation, four case studies that had a major impact on the coast of São Paulo in 2020 were selected. Because storm surges that reach the coast may cause coastal flooding, precipitation forecasts were also evaluated. The mesoscale Eta model produces forecasts with a 5 km resolution for up to an 84 h lead time. The model was set up in a region that covers part of southeast and south Brazil. The ERA5 reanalysis was used to describe the large-scale synoptic conditions and to evaluate the weather forecasts. The cases showed a region in common between 35° S, 40° S and 35° W, 45° W where the low-pressure center deepened rapidly on the day before the highest waves reached the coast of São Paulo, with a mostly eastward, rather than northeastward, displacement of the associated surface cyclone and minimal or no tilt with height. The winds on the coast were the strongest on the day before the surge reached the coast of São Paulo, and then the winds weakened on the day of the maximum wave height. The pattern of the mean sea-level pressure and 10 m wind in the 36 h, 60 h, and 84 h forecasts agreed with the ERA5 reanalysis, but the pressure was slightly underestimated. In contrast, the winds along the coast were slightly overestimated. The 24 h accumulated precipitation pattern was also captured by the forecast, but was overestimated, especially at high precipitation rates. The 36 h forecasts showed the smallest error, but the growth in the error for longer lead times was small, which made the 84 h forecasts useful for driving wave models and other local applications, such as an early warning system.

Zinc tungstate integrated onto zinc oxide h-MDs-based flexible piezoelectric power device: A new energy technology for real-time indoor laboratory safety warning system

With the rapid development of research world, the number of researchers and research laboratories is increasing rapidly. Simultaneously, accidents in laboratories are on the rise, drawing significant attention from everyone. To address this issue, the current study demonstrates a real-time safety early warning alarm system aimed at preventing accidents and enhancing safety management in laboratories. We developed a zinc tungstate nanoparticles (NPs) integrated on zinc oxide hexagonal microdiscks (h-MDs) (ZnO–ZnWO4@PDMS) composite based on a novel flexible piezoelectric nanogenerator (FPENG). The observed piezoelectric response was 1.32 V, which is ∼4 times better than that of pure ZnWO4, owing to the synergistic effect of ZnO–ZnWO4@PDMS. When subjected to finger-tapping motion, the FPENG can charge a 1 μF capacitor, and it reached 81 mV within 56 s. Subsequently, the light-emitting diodes (LEDs) are powered with amplification. The FPENG system was demonstrated and validated through real-time application in the laboratory setting, marked as a “caution/restricted” line. The application of the system demonstrated its capability to simultaneously illuminate LEDs and sound alarms when people touch or cross the caution line. Furthermore, the proposed FPENG exhibited ∼660 mV even ∼90 % humidity, with no noticeable electrical changes under different solvents, confirming that the proposed FPENG is not affected by varying humidity and various indoor conditions. These outstanding results and real-time demonstrations unequivocally establish the feasibility of this system in contexts such as bank lockers, metro systems, hilly areas, and more.

Application of BIM+GIS Integration Technology in the Construction of Urban Intelligent Water Informatization

Urban intelligent water informatization is the process of managing and operating urban water systems with the use of contemporary information and communication technology in order to increase productivity, security, and sustainability. Establishing the appropriate infrastructure is the initial stage in building an intelligent water system so that management can choose wisely how to manage and operate the water system. Building information modeling (BIM) is a digital depiction of a building's or infrastructure's structural and functional details. In most metropolitan regions, a fast development phase is predicted to cause several water issues, including water resource shortages, floods, poor drainage, and water resource contamination. As a result, a practical approach is required for urban intelligent water information technology with the help of artificial intelligence techniques. Intelligent water systems may be built using Building Information Modeling (BIM) software to increase the system's effectiveness, stability, and durability. By offering a potent tool for storing and analyzing geographical data connected to the water distribution system, a geographic information system (GIS) can play a crucial role in the development of urban intelligent water informatization using ArcGIS mapping Software. The Internet of Things (IoT) system is used to implement intelligent water informatization, hardware implementation using ESP32, and software development using MQTT Broker and Node-Red. As a result, we recommended utilizing BIM+GIS integration technology for building urban intelligent water informatization. The suggested system is evaluated using management efficiency, urban flood control, innovative Construction, intensive management, and computation cost. In this research, the main objective is to identify where there are water leaks broken pipes and early warning system and monitoring system for flash flood disasters in urban areas. The result findings show that the integration of BIM and GIS provides a comprehensive platform for managing complex water informatization infrastructure projects and maximizes efficiency and sustainability.

High-resolution impact-based early warning system for riverine flooding

Despite considerable advances in flood forecasting during recent decades, state-of-the-art, operational flood early warning systems (FEWS) need to be equipped with near-real-time inundation and impact forecasts and their associated uncertainties. High-resolution, impact-based flood forecasts provide insightful information for better-informed decisions and tailored emergency actions. Valuable information can now be provided to local authorities for risk-based decision-making by utilising high-resolution lead-time maps and potential impacts to buildings and infrastructures. Here, we demonstrate a comprehensive floodplain inundation hindcast of the 2021 European Summer Flood illustrating these possibilities for better disaster preparedness, offering a 17-hour lead time for informed and advisable actions.

Effect of bathymetry data on tsunami wave ray tracing in the western Banten sea

The lack of tsunami wave warning during the Anak Krakatau collapse in 2018 resulted in devastating damage to coastal areas. The tsunami wave arrived in the coastal areas approximately half an hour after the collapse. As a tsunami wave can travel abruptly, an early warning system should warn faster. However, a warning system based on a sophisticated hydrodynamic model in real-time would take time to conduct the numerical tsunami wave simulation. Hence, this study proposes a fast and reliable estimation of tsunami wave propagation through a classic ray tracing analysis. We use two ray-tracing methods to investigate the tsunami wave propagation from Anak Krakatau to the western Banten Sea. The first method follows Snell’s law, considering dispersive waves, and the second assumes non-dispersive waves based on the ray tracing equations, considering the Earth’s sphericity. Both methods are quantitatively evaluated by comparing the travel time measured at Anyer and Marina Jambu. This study finds that non-dispersive wave tracing performs a shorter computational time and slightly better prediction of tsunami wave propagation than dispersive-based wave tracing, with a relative absolute difference of the travel time of 17.9–26.7% in Anyer and 3.6–5.2% in Marina Jambu. This study also confirms the importance of bathymetry validity in wave ray tracing. Two regional bathymetry datasets with a mean difference of less than 5 m result in different wave ray tracing, in which one dataset does not produce the wave ray path toward the Panaitan Island. Based on bathymetric surveys in Anyer and Marina Jambu, the national bathymetry data (BATNAS) has shown its superiority to being used as a bathymetry in the ray-tracing process, with correlations of 81% and 93% in both areas compared to other available bathymetry datasets. We summarize that reliable bathymetry data and the non-dispersive ray tracing method can be used as an initial estimation of tsunami wave propagation efficiently.