The application of data fusion for disaster prediction using GPT (Generative Pre-trained Transformer) is covered in this study. Data fusion is the process of merging information from several sources including social media, sensor data, and simulation data, in order to increase the precision of catastrophe prediction models. Through the utilization of GPT, an artificial intelligence language model, data fusion enables a thorough examination and amalgamation of data from disparate fields, hence producing more resilient forecasts. GPT models may be used to recognize geographical descriptions from social media postings and identify cell kinds using information about marker genes. Proactive communication with impacted people in times of catastrophe is made possible by the integration of GPT with social media monitoring. GPT models may significantly enhance disaster preparedness, response, prediction, and recovery by gathering pertinent data from many sources and modelling various situations.

Leadership plays a vital role in empowering communities to reduce disaster risks, yet there is limited information on this topic. This study presents a bibliometric analysis concentrated on leadership in disaster management, specifically addressing natural hazards. Utilizing the Scopus database, a total of 152 documents published between 1999 and 2023 were identified. The growth in publications annually has shown an upward trend, particularly since 2012. The International Journal of Disaster Risk Reduction emerged as the primary source. Yet, the most impactful documents in terms of citations predominantly originate from non-disaster-related journals. There remains a notable imbalance in contributions from authors across countries, with the United States of America and Australia exerting significant influence in both document quantity and citation count. Keyword analysis revealed various disasters as pivotal moments driving scholarly advancements, notably Hurricane Katrina in 2005. The role of leadership is not limited to formal institutions and emergency responses but can extend to various actors and sectors in all stages of disaster management. Ultimately, this bibliometric study sheds light on the progression and emerging focal points within the research landscape of leadership in disaster management.

Train is considered as one of the primary means of transport for the people across various regions in India. With a route length of around 68000 km, the Indian rail network is the fourth longest in the world and millions use its services daily. Despite having an extensive network and infrastructure, train accidents are not uncommon in India. The recent incident in Balasore district, Odisha, is the deadliest train disaster in over two decades. Nearly 295 people lost their lives and several hundred got injured in the triple train crash. This incident has brought attention to the safety measures and infrastructure of the country’s railway system. Although the Indian railway systems have seen significant development, the lack of a robust signalling system remains a major issue for such massive mishaps. This study discusses major train accidents with their causes and effects. Then, it discusses train disaster categories and the railway disaster management plan. It also provides a brief review of State-of-the-Art safety systems to prevent train accidents. Further, this study highlights the role of signalling and telecommunication systems in the safe and speedy movement of trains.

This study proposes to use geospatial techniques to assess coastal geomorphological changes along the southeast coast of India, from Cuddalore to Nagapattinam. The results will provide insights into regional morphology changes and will advance our understanding of coastal evolution, sea level variations and quaternary geomorphic processes. The coastal geomorphological units of the study area comprise of the coastal zone and an older deltaic plain. Most coastal zone features have developed through erosion, accretion and other natural processes. These features can be classified as depositional or erosional. By analyzing satellite imagery from 1980 to 2020, the study identified changes in coastal geomorphological features. This information can be used to locate mineral deposits, hydrocarbon locales, groundwater targets and sites for artificial recharge and saltwater intrusion mitigation. It can also affect coastal zone management decisions.

Disaster is a significant problem that extensively affects society and the community. Predicting the effects of a disaster is difficult for several reasons. The primary aim of this study is to evaluate the effects of disasters across several timeframes, ranging from immediate to long-term. To construct a plausible model, the proposed solution considers the available disaster datasets from various agencies (e.g. SMS, ISC, NDMC etc.). A methodology for assessing the long-term effects of disasters utilizing well-liked machine learning techniques is presented here. It consists of the algorithms for Decision Tree, Random Forest, Gradient Boost Decision Tree and XG Boost. The algorithms' classification accuracy for the provided data sets is 56%, 63%, 83% and 91% respectively. The proposed work also examines the various levels of disaster severity and suggests solutions for each level to improve preparedness and response measures.

Globally, flooding is a common natural disaster affecting individuals and communities by causing substantial loss of individual properties, public infrastructure, physical environment and even threaten human lives. It is often triggered by the factors like heavy rainfall, storms, hurricanes, flash floods which brings devastating consequences for the affected peoples worldwide including displacement of people, loss of life, health issues and financial losses. During 1995-2015, it affected nearly 2.3 billion people and was responsible for 157,000 deaths globally. Therefore, it requires a strong disaster response framework to mitigating its short and long-term impacts. This response framework should be based on risk assessment, response strategies and building disaster resilience to reduce the people’s overall vulnerability to flooding disasters. The current study aims to conceptualize the flooding disaster through the social vulnerability theory, risk society theory and adaptive governance theory so that policymakers and governments can develop a deeper understanding of the complex factors that contribute to flooding disasters and prepare effective strategies to tackle the disaster effectively. This may include implementing effective preparedness and response measures such as early warning systems, emergency evacuation plans and adequate infrastructure which can help mitigate the risks and subsequent impacts of flooding on individuals and communities.

It is hoped that a change in the paradigm of disaster management that prioritizes disaster risk management will prevent adverse impacts when a disaster occurs. One of the actions to realize the new paradigm is to conduct a disaster risk mitigation study. Success in disaster mitigation from the results of previous research is involving community participation. This study aimed to examine the benefits of a disaster mitigation approach through community participation. This study used a literature review research method which originates from international and national journals as well as books related to community participation-based disaster mitigation. The results of this study are: a) Community involvement is a solution in disaster mitigation which is achieved by conducting risk assessments and planning for disaster risk reduction for community capacity building in disaster mitigation; b) Community-based disaster mitigation is a project that saves costs, promotes community self-help so that it can support sustainable development; c) The key to successful implementation of community-based disaster mitigation applies a practical methodology with a bottom-up approach, creating an organizational structure that accommodates formal and informal types, prioritizing forms of disaster awareness through education and training used local wisdom, empowering the abilities of volunteers and developing partnerships. with stakeholders.

Considering the large geographical landmass of India along with cultural diversity, an exercise to understand issues impacting disaster response apparatus in India is very necessary. Listing out these issues and understanding the root cause of these issues, disasters, both natural and man-made, have been a recurrent phenomenon in India. The country has been experiencing floods, earthquakes, cyclones and other disasters for years and each event has posed new challenges and issues to the Nation’s disaster response system. Even though India has made strides in the management of natural disasters, there are still many substantial obstacles and problems that need to be resolved. One of the main challenges is the lack of coordination and communication between different agencies involved in disaster response. There is a pressing need for a disaster management strategy that is more integrated and combines all stakeholders and leverages technology to improve communication and coordination. Another challenge is the limited availability of resources and funding for disaster response. Many people are not aware of the risks they face and are not prepared to respond to emergencies. To enhance preparedness and reaction times, there is a pressing need for more education and awareness initiatives. The present study is primarily focused on finding out the response available within the system and its impact. suggestions, if any to improvise the system.

This study focuses on the spatiotemporal drought analysis in the driest region of southeastern Morocco, specifically the Imiter region within the eastern Anti Atlas region. The region has witnessed various climate change events in recent years, necessitating a comprehensive investigation of the relationship between climate and environmental conditions. To achieve this, Landsat 7, 8 and 9 satellite data were utilized for drought analysis. Additionally, satellite-based drought indices including the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI), were employed to assess the extent of drought evolution. Over a period of two decades (from 1998 to 2022), 24 annual spatial maps were generated for each index to identify the most affected areas. The analysis of NDVI and VCI from 1998 to 2011 revealed significant year-to-year variability, attributed to various factors such as declining piezometric levels, deforestation and mining activities. These findings contribute to a better understanding of drought dynamics in the study area and highlight the importance of monitoring environmental conditions for effective drought management and mitigation strategies.

Watershed located in a Hilly-plain environment is more susceptible for soil erosion. Loss of fertile soil in agricultural land hampers the life of the farmers of the region. Identification of the vulnerable areas and devising strategies to minimize the damage caused due to soil erosion are important to address the problems faced by the agrarian society. In our current work, drainage morphometric parameters were evaluated to assess the soil erosion susceptibility of the four sub-watersheds (SW01, SW02, SW03 and SW04) of the Yagachi watershed located in the Hemavathi river basin in Karnataka State using Remote Sensing (RS) and Geographical Information System (GIS). Seventeen morphometric parameters namely Area, Stream Order, Mean Bifurcation Ratio, Drainage Density, Stream Frequency, Texture Ratio, Infiltration Number, Compactness Co-Efficient, Length of Overland Flow, Elongation Ratio, Circulatory Ratio, Form Factor, Relief Ratio, Shape Factor, Basin Relief, Dissection Index and Ruggedness Number were considered. The priority rank and category for each sub-watershed were assigned based on Compound Factor (CF) value. The sub-watersheds with the lowest CF value are most susceptible to erosion and need highest priority for the soil conservation measures. Based on CF values, the sub-watersheds were categorized into 4 priority groups: ‘Very High’ priority (<2.00), ‘High’ priority (2.01–2.50), ‘Moderate’ priority (2.51–3.00) and ‘Low’ priority (≥3). Out of 4 sub-watersheds, SW03 falls in ‘Very High’ priority category in terms of erosion susceptibility and needs appropriate soil conservation measures in order to arrest fertile soil erosion and increase agricultural produce output.