Search Results (34)

With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. In recent years, the application of deep learning technology in ionospheric modeling has achieved breakthrough advancements, significantly impacting navigation, communication, and space weather forecasting. Nevertheless, due to limitations in observational networks and the dynamic complexity of the ionosphere, deep learning-based ionospheric models still face challenges in terms of accuracy, resolution, and interpretability. This paper systematically reviews the development of deep learning applications in ionospheric modeling, summarizing findings that demonstrate how integrating multi-source data and employing multi-model ensemble strategies has substantially improved the stability of spatiotemporal predictions, especially in handling complex space weather events. Additionally, this study explores the potential of deep learning in ionospheric modeling for the early warning of geological hazards such as earthquakes, volcanic eruptions, and tsunamis, offering new insights for constructing ionospheric-geological activity warning models. Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. Overall, deep learning provides a powerful tool for ionospheric modeling and indicates promising prospects for its application in early warning systems and future research. Full article

The available historical documents for the city of Alexandria indicate that it was damaged to varying degrees by several (historical and instrumentally recorded) earthquakes and by highly destructive tsunamis reported at some places along the Mediterranean coast. In this work, we applied the neo-deterministic seismic hazard analysis (NDSHA) approach to the Alexandria metropolitan area, estimating ground motion intensity parameters, e.g., peak ground displacement (PGD), peak ground velocity (PGV), peak ground acceleration (PGA), and spectral response, at selected rock sites. The results of this NDSHA zonation at a subregional/urban scale, which can be directly used as seismic input for engineering analysis, indicate a relatively high seismic hazard in the Alexandria region (e.g., 0.15 g), and they can provide an essential knowledge base for detailed and comprehensive seismic microzonation studies at an urban scale. Additionally, we established detailed tsunami hazard inundation maps for Alexandria Governorate based on empirical relations and considering various Manning’s Roughness Coefficients. Across all the considered scenarios, the average estimated time of arrival (ETA) of tsunami waves for Alexandria was 75–80 min. According to this study, the most affected sites in Alexandria are those belonging to the districts of Al Gomrok and Al Montazah. The west of the city, called Al Sahel Al Shamally, is less affected than the east, as it is protected by a carbonate ridge parallel to the coastline. Finally, we emphasize the direct applicability of our study to urban planning and risk management in Alexandria. Our study can contribute to identifying vulnerable areas, prioritizing mitigation measures, informing land-use planning and building codes, and enhancing multi-hazard risk analysis and early warning systems. Full article

Tsunamis are destructive oceanic hazards caused by underwater disturbances, mainly earthquakes. A deterministic tsunami hazard assessment for the United Arab Emirates (UAE), due to the Makran Subduction Zone (MSZ), was conducted based on the history of earthquakes in the region and considering the rapid development and urbanization of the east coast of the UAE. A variety of earthquake source scenarios was modeled, involving moment magnitudes of 8.2, 8.8, and 9.2. Tsunami travel time (TTT), run-up, flow depth, and inundation maps were generated to pinpoint the areas susceptible to tsunami hazards for the eastern coastal cities of Kalba, Al Fujairah, Khor Fakkan, and Dibba. The results show that the worst-case M_w 9.2 earthquake in a full MSZ rupture scenario resulted in an average TTT of 37 min, a maximum run-up height of 2.55 m, a maximum flow depth of 2.2 m, and a maximum inundation distance of 253 m on the east coast of the UAE. The M_w 8.2 western MSZ earthquake and the M_w 8.8 eastern MSZ earthquake scenarios were of less significant impact. These findings provide new insights into tsunami hazard assessment and are expected to play a vital role in advancing sustainable development in the region by providing key information for stakeholders and authorities as they highlight the need for enhanced tsunami mitigation and preparedness measures to reduce the potential impact of future tsunamis on the UAE. Full article

Tsunamis are devastating natural phenomena that cause extensive damage to both human life and infrastructure. To mitigate such impacts, tsunami early warning systems have been deployed globally. South Korea has also initiated a project to install a tsunami warning system to monitor its surrounding seas. To ensure reliable warning decisions, various types of data must be combined, but efficiently transmitting heterogeneous data poses a challenge due to the unique characteristics of underwater acoustic communication. Therefore, this paper proposes a Hybrid Duplex Medium Access Control (HDMAC) protocol designed for a tsunami warning system, with a specific focus on heterogeneous data transmission. HDMAC efficiently handles both seismic and environmental data by utilizing hybrid duplexing, which combines frequency duplex for seismic data with time duplex for environmental data. The protocol addresses the distinct transmission requirements for each data type by optimizing channel utilization through a group Automatic Repeat request (ARQ) scheme and packet size adjustment. Theoretical analysis predicts that HDMAC can achieve a channel utilization of up to 0.91 in smaller networks and 0.64 in larger networks. HDMAC is validated through simulations, and the simulation results closely match these predictions. The simulation results demonstrate the efficiency of HDMAC in supporting real-time submarine earthquake monitoring systems. Full article

Real-time data transmission and reliable operation are essential for a tsunami monitoring system to provide effective data. In this study, a novel real-time tsunami monitoring system is designed based on a submersible mooring system. This system is equipped with a data acquisition and tsunami wave identification algorithm, which can collect the measured data of the pressure sensor and detect a tsunami wave in real time. It adopts the combination design of underwater inductive coupling transmission and a redundant BeiDou communication device on the water surface to ensure the reliability of real-time data transmission. Compared with traditional tsunami monitoring buoys, it has the advantages of reliable communication, good concealment, high security, and convenient deployment, recovery, and maintenance. The results of laboratory and sea tests show that the system has high reliability of data transmission, stable overall operation of the system, and good application prospects in the field of real-time tsunami monitoring and early warning. Full article

This study explores the transformative potential of artificial intelligence (AI) in revolutionizing earthquake risk mitigation across six key areas. Unlike traditional approaches, this paper examines how AI-driven innovations can uniquely enhance early warning systems, enabling real-time structural health monitoring, and providing dynamic, multi-hazard risk assessments that seamlessly integrate seismic data with other natural hazards such as tsunamis and landslides. It introduces groundbreaking applications of AI in earthquake-resilient design, where generative design algorithms and predictive analytics create structures that optimally balance safety, cost, and sustainability. The study also presents a novel discussion on the ethical implications of AI in this domain, stressing the critical need for transparency, accountability, and bias mitigation. Looking forward, the manuscript envisions the development of advanced AI platforms capable of delivering real-time, personalized risk assessments, immersive public training programs, and collaborative design tools that adapt to evolving seismic data. These innovations promise not only to significantly enhance current earthquake preparedness but also to pave the way toward a future where the societal impact of earthquakes is drastically reduced. This work underscores the potential of AI’s role in shaping a safer, more resilient future, emphasizing the importance of continued innovation, ethical governance, and collaborative efforts. Full article

Owing to global climate change or the ever-more frequent human activities in the offshore areas, it is highly probable that an imbalance in the offshore ecosystem has been induced. However, the importance of maintaining and protecting marine ecosystems’ balance cannot be overstated. In recent years, various marine disasters have occurred frequently, such as harmful algal blooms (green tides and red tides), storm surge disasters, wave disasters, sea ice disasters, and tsunami disasters. Additionally, overpopulation of certain marine organisms (particularly marine faunas) has led to marine disasters, threatening both marine ecosystems and human safety. The marine ecological disaster monitoring system in China primarily focuses on monitoring and controlling the outbreak of green tides (mainly caused by outbreaks of some Ulva species) and red tides (mainly caused by outbreaks of some diatom and dinoflagellate species). Currently, there are outbreaks of Cnidaria (Hydrozoa and Scyphozoa organisms; outbreak species are frequently referred to as jellyfish), Annelida (Urechis unicinctus Drasche, 1880), Mollusca (Philine kinglipini S. Tchang, 1934), Arthropoda (Acetes chinensis Hansen, 1919), and Echinodermata (Asteroidea organisms, Ophiuroidea organisms, and Acaudina molpadioides Semper, 1867) in China. They not only cause significant damage to marine fisheries, tourism, coastal industries, and ship navigation but also have profound impacts on marine ecosystems, especially near nuclear power plants, sea bathing beaches, and infrastructures, posing threats to human lives. Therefore, this review provides a detailed introduction to the marine organisms (especially marine fauna species) causing marine biological disasters in China, the current outbreak situations, and the biological backgrounds of these outbreaks. This review also provides an analysis of the causes of these outbreaks. Furthermore, it presents future prospects for marine biological disasters, proposing corresponding measures and advocating for enhanced resource utilization and fundamental research. It is recommended that future efforts focus on improving the monitoring of marine biological disasters and integrating them into the marine ecological disaster monitoring system. The aim of this review is to offer reference information and constructive suggestions for enhancing future monitoring, early warning systems, and prevention efforts related to marine ecological disasters in support of the healthy development and stable operation of marine ecosystems. Full article

Tsunami monitoring and early warning systems are mainly established to deal with seismogenic tsunamis generated by sudden seafloor fault displacement. However, a global tsunami triggered by the 2022 Tonga volcanic eruption promoted the need for tsunami early warning and hazard mitigation of non-seismogenic tsunamis in coastal countries. This paper studied the spatiotemporal distribution characteristics of historical volcanic tsunamis and summarized high-risk areas of volcanic tsunamis. The circum southwestern Pacific volcanic zone, including the Sunda volcanic belt and the Indo-Australian plate, is a concentrated area of active volcanoes and major volcanic tsunamis. In addition, the challenges associated with adapting seismogenic tsunami techniques for use in the context of volcanic tsunamis were elucidated. At the same time, based on historical records and post-disaster surveys, typical historical volcanic tsunami events and involved mechanisms were summarized. The results show that a majority of volcanic tsunamis may involve multiple generation mechanisms, and some mechanisms show geographical distribution characteristics. The complexity of volcanic tsunami mechanisms poses challenges to tsunami early warning by measuring tsunami sources to evaluate the possible extent of impact, or using numerical modeling to simulate the process of a tsunami. Therefore, a concise overview of the lessons learned and the current status of early warning systems for volcanic tsunamis was provided. Finally, a conceptual scheme of monitoring systems for volcanic tsunamis based on historical volcanoes, real-time volcanic eruption information and sea level data, as well as remote sensing images, was presented. Full article

Although tsunamis occur less frequently compared to some other natural disasters, they can be extremely devastating in the nearshore environment if they occur. An earthquake of magnitude 6.9 Mw occurred on 30 October 2020 at 12:51 p.m. UTC (2:51 p.m. GMT+03:00) and its epicenter was approximately 23 km south of İzmir province of Turkey, off the Greek island of Samos. The tsunami event triggered by this earthquake is known as the 30 October 2020 İzmir-Samos (Aegean) tsunami, and in this paper, we study the hydrodynamics of this tsunami using some of these artificial intelligence (AI) techniques applied to observational data. More specifically, we use the tsunami time series acquired from the UNESCO data portal at different stations of Bodrum, Syros, Kos, and Kos Marina. Then, we investigate the usage and shortcomings of the Long Short Term Memory (LSTM) DL technique for the prediction of the tsunami time series and its Fourier spectra. More specifically we study the predictability of the offshore water surface elevation dynamics, their spectral frequency and amplitude features, possible prediction success and enhancement of the accurate early prediction time scales. The uses and applicability of our findings and possible research directions are also discussed. Full article

Coastal inundation has recently started to require significant attention worldwide. The increasing frequency and intensity of extreme events (sea storms, tsunami waves) are highly stressing coastal environments by endangering a large number of residential areas, ecosystems, and tourist facilities, and also leading to potential environmental risks. Predicting such events and the generated coastal flooding is thus of paramount importance and can be accomplished by exploiting the potential of different tools. An example is the combination of remote sensors, like marine radars, with numerical models. Specifically, while instruments like X-band radars are able to precisely reconstruct both wave field and bathymetry up to some kilometers off the coast, wave-resolving Boussinesq-type models can reproduce the wave propagation in the nearshore area and the consequent coastal flooding. Hence, starting from baseline simulations of wave propagation and the conversion of water elevation results into radar images, the present work illustrates the reconstruction of coastal data (wave field and seabed depth) using a specifically suited data processing method, named the “Local Method”, and the use of such coastal data to run numerical simulations of coastal inundation in different scenarios. Such scenarios were built using two different European beaches, i.e., Senigallia (Italy) and Oostende (Belgium), and three different directional spreading values to evaluate the performances in cases of either long- or short-crested waves. Both baseline and inundation simulations were run using the FUNWAVE-TVD solver. The overall validation of the methodology, in terms of maximum inundation, shows its good performance, especially in cases of short-crested wind waves. Furthermore, the application on Oostende Beach demonstrates that the present methodology might work using only open-access tools, providing an easy investigation of coastal inundation and potential low-cost integration into early warning systems. Full article

In this study, three worst-case credible tsunamigenic scenarios (Mw8.0) from Xiamen fault 1 (XF 1), Xiamen fault 2 (XF 2) and Xiamen fault 3 (XF 3) located off the coast of Xiamen were selected to assess the local tsunami hazard for Xiamen city, Fujian province, China. The GeoClaw model was utilized to compute the propagation and inundation of the tsunami for each scenario. The simulation results show that local tsunamis from XF 1–3 hit Xiamen within 1.5 h of earthquakes. The highest level of tsunami hazard in Xiamen is level II, which corresponds to an inundation depth ranging from 1.2 to 3.0 m. The areas with tsunami hazard level II in each scenario are primarily concentrated in the coastal areas of southern Haicang district and eastern Siming district, which are in the primary propagation direction of the tsunami. Since XF 2 and XF 3 are aligned almost parallel to the coastline of Xiamen, local tsunamis from XF 2 and XF 3 could cause more serious hazards to the coastal areas of Xiamen city. This work provides a typical case for researchers to understand the local tsunami hazard assessment for coastal cities. The research results can provide scientific references for the development of tsunami hazard assessment and early warning systems for coastal cities in southeastern China. Full article

Earthquakes are among the most impressive natural phenomena with very high potential to set off a chain of effects that significantly affects public health through casualties and injuries. Related disasters are attributed not only to the strong ground motion and coseismic phenomena but also to secondary effects, comprising mainly landslides and tsunamis, among others. All these can create harsh conditions favorable for the emergence of infectious diseases that are capable of causing additional human and economic losses and disruption of the emergency and recovery process. The present study comprises an extensive narrative review of the existing literature on the earthquake-triggered infectious diseases recorded worldwide, along with their symptoms, causative pathogens, associated risk factors, most vulnerable population groups, and prevention strategies. Respiratory, gastrointestinal, and vector-borne diseases, as well as wound and skin infections, are mainly recorded among the earthquake-affected population. Measures for effectively preventing earthquake-triggered infectious diseases are also proposed. One of the widely proposed measures is the establishment of a proper disease surveillance system in order to immediately and effectively identify the pre- and post-disaster occurrence of infectious diseases. This approach significantly contributes to disease trends monitoring, validation of early warning, and support of the emergency response and recovery actions. Full article

The current prototype of a fully automatic earthquake tsunami hazard assessment system, “Tsunami Observer”, is described. The transition of the system to the active phase of operation occurs when information about a strong earthquake (M_w ≥ 6.0) is received. In the first stage, the vector field of coseismic displacements of the Earth’s crust is calculated by using the Okada formulas. In the calculations, use is made of data on the coordinates, the seismic moment, the focal mechanism, and the depth of the earthquake, as well as empirical patterns. In the second stage, the initial elevation of the water surface at the tsunami’s focus is determined with the vector field of coseismic displacements of the bottom and the distribution of ocean depths, and the earthquake’s potential energy is calculated. In the third stage, the intensity of the tsunami is estimated on the Soloviev–Imamura scale in accordance with the magnitude of the potential energy by using the empirical relationship that is obtained as a result of a statistical analysis of historical tsunami events. In the final stage, if the energy exceeds the critical value of 10⁹ J, a numerical simulation of the tsunami is performed, which allows the determination of the predominant directions of wave energy propagation and estimation of the runup height on the nearest coast. In this work, data on the operation of the system over the last 3 years are presented. Full article

With the economic crisis going around the world, a new approach, “build back better”, has been adopted as a recovery package for various systems. The tsunami detection and warning system is one such system, crucial for saving human lives and infrastructure. While designing a tsunami detection system, the social, economic, and geographical circumstances are considered to be vital. This research is focused on designing a low-cost early warning system mainly for underdeveloped countries, which are more prone to tsunami damage due to a lack of any reliable early warning and detection systems. Such countries require proper cost-effective solutions to address these issues. Previous research has shown that the existing systems are either very costly or hard to implement and manage. In this study, we present a wireless sensor networking model, which is an optimized model in terms of cost, delay, and energy consumption. This research contemplates the techniques and advantages of the intelligence of marine animals. We propose a fuzzy logic-based approach for early tsunami detection, using electromagnetic and pressure sensors, based on the behavioral attributes of turtles and real-time values of earthquakes and water levels. Full article

Sea level measurements are of critical importance in the verification of tsunami generation. When a large earthquake occurs in a subduction zone and the Regional Tsunami Service Providers of UNESCO/IOC issue alerts, sea level measurements are used to verify tsunami generation and take further actions (i.e., the evacuation of coastal areas). However, in some cases, if the tsunami source is very close to the coast, there is not enough time between the identification of an event and the issue of alerting bulletins. In addition, when the tsunami is not generated by a large earthquake but rather an atypical source (i.e., landslide or volcanic eruption) or prior information from the earthquake is not available before the arrival of the tsunami, it is of vital importance to have other means for the verification of tsunami generation. The algorithm presented in this paper, already installed in several operational devices, is capable of acquiring, processing and moving data back into the data server of the Joint Research Centre of the European Commission (EC-JRC) or any other relevant database; it can also be used for any sea level measurement of interest with corresponding triggering criteria. Full article