Revealed: Chilling text NASCAR star Greg Biffle's wife sent to her mom just minutes before tragic plane crash'Old age' doesn't kill us... scientists reveal true causes of death Immutable: I can't get enough of Melania, the Real Housewife of Washington, says JAN MOIR The tiny diet change that brought down my sky-high cholesterol WITHOUT statins or drugs. Mike was told he risked a heart attack or stroke. CNBC anchor who slammed Trump's tariffs as'insane' stunned live on air as inflation figures send shockwaves through Wall Street Dramatic bodycam video shows moment suspected kidnapper is arrested after 40 years on the run... as her neighbor thinks arrest is a joke Rob Reiner's'petrified' parting words about son Nick at Conan O'Brien party... and why his haunted A-list friends can't stop talking about it Reiner family bombshell as insiders reveal who is paying for Nick's celebrity lawyer... their secret motive... and who will REALLY inherit $200m fortune Doctors said my hip pain was just tendinitis from sitting all day at work. The real cause may kill me... they had left it far too late Bondi hero is handed $2.5million cheque in his hospital bed - as hero asks unbelievable question Pete Davidson is a dad! Kim Kardashian's ex welcomes first child with model girlfriend Elsie Hewitt Mica Miller's pastor husband is indicted for shocking acts before his wife was killed days after filing for divorce Transportation Secretary Sean Duffy's daughter slams TSA as'unconstitutional' after she was subjected to a pat down for refusing to go through the body scanner Jeffrey Epstein attended dinner with tech billionaires three years after he was convicted of sex crimes - as new photos of the event are released from pedophile's estate Terrifying maps break down exactly who is at risk of new'super flu' exploding across America... as doctors reveal symptoms to really worry about READ MORE: Blind psychic Baba Vanga's world-changing 2025 prophecy feared to occur in just DAYS A blind psychic, who allegedly foretold 9/11 and the Covid pandemic, shared several world-changing prophecies for 2026 before her death nearly 30 years ago. Baba Vanga was a Bulgarian mystic and clairvoyant who became a cult figure among conspiracy theorists after several of her eerie pronouncements were proved true.

Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehrans critical District 6.

Post-traumatic stress disorder (PTSD) is a significant mental health challenge that affects individuals exposed to traumatic events. Early detection and effective intervention for PTSD are crucial, as it can lead to long-term psychological distress if untreated. Accurate detection of PTSD is essential for timely and targeted mental health interventions, especially in disaster-affected populations. Existing research has explored machine learning approaches for classifying PTSD, but many face limitations in terms of model performance and generalizability. To address these issues, we implemented a comprehensive preprocessing pipeline. This included data cleaning, missing value treatment using the SimpleImputer, label encoding of categorical variables, data augmentation using SMOTE to balance the dataset, and feature scaling with StandardScaler. The dataset was split into 80\% training and 20\% testing. We developed an ensemble model using a majority voting technique among several classifiers, including Logistic Regression, Support Vector Machines (SVM), Random Forest, XGBoost, LightGBM, and a customized Artificial Neural Network (ANN). The ensemble model achieved an accuracy of 96.76\% with a benchmark dataset, significantly outperforming individual models. The proposed method's advantages include improved robustness through the combination of multiple models, enhanced ability to generalize across diverse data points, and increased accuracy in detecting PTSD. Additionally, the use of SMOTE for data augmentation ensured better handling of imbalanced datasets, leading to more reliable predictions. The proposed approach offers valuable insights for policymakers and healthcare providers by leveraging predictive analytics to address mental health issues in vulnerable populations, particularly those affected by disasters.

The number of people living in urban areas has tripled in the last 50 years, meaning when a major natural disaster such as an earthquake strikes a city, more lives are in danger. Meanwhile, the strength and frequency of extreme weather events has increased--a trend set to continue as the climate warms. That is spurring efforts around the world to develop a new generation of earthquake monitoring and climate forecasting systems to make detecting and responding to disasters quicker, cheaper, and more accurate than ever. On Nov. 6, at the Barcelona Supercomputing Center in Spain, the Global Initiative on Resilience to Natural Hazards through AI Solutions will meet for the first time. The new United Nations initiative aims to guide governments, organizations, and communities in using AI for disaster management.

Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most critical areas that need support. Nonetheless, manually inspecting large areas is time-consuming and requires domain expertise. Thanks to their accuracy, generalization capabilities, and highly parallelizable workload, Deep Neural Networks (DNNs) provide an excellent way to automate this task. Nevertheless, there is a scarcity of VHR data pertaining to conflict situations, and consequently, of studies on the effectiveness of DNNs in those scenarios. Motivated by this, our work extensively studies the applicability of a collection of state-of-the-art Convolutional Neural Networks (CNNs) originally developed for natural disasters damage assessment in a war scenario. To this end, we build an annotated dataset with pre- and post-conflict images of the Ukrainian city of Mariupol. We then explore the transferability of the CNN models in both zero-shot and learning scenarios, demonstrating their potential and limitations. To the best of our knowledge, this is the first study to use sub-meter resolution imagery to assess building damage in combat zones.

In the small village of Ogba-Ojibo in central Nigeria, sitting at the confluence of two of the nation's largest rivers – the Niger and Benue – 27-year-old Ako Prince Omali is counting the steps carved out of the dirt, which lead down the loam-coloured banks of the river Niger. This river bank, dotted with tufts of spiky grass, is where villagers come to fish or wash produce and laundry. Just last week, three of the steps were submerged during one night of rain, which raised the water level by about five metres. Normally, you can count seven steps down into the river. Now, only four remain above the surface of the water, the sticks bracing the muddy steps having washed away in the deluge.

Emotion mining has become a crucial tool for understanding human emotions during disasters, leveraging the extensive data generated on social media platforms. This paper aims to summarize existing research on emotion mining within disaster contexts, highlighting both significant discoveries and persistent issues. On the one hand, emotion mining techniques have achieved acceptable accuracy enabling applications such as rapid damage assessment and mental health surveillance. On the other hand, with many studies adopting data-driven approaches, several methodological issues remain. These include arbitrary emotion classification, ignoring biases inherent in data collection from social media, such as the overrepresentation of individuals from higher socioeconomic status on Twitter, and the lack of application of theoretical frameworks like cross-cultural comparisons. These problems can be summarized as a notable lack of theory-driven research and ignoring insights from social and behavioral sciences. This paper underscores the need for interdisciplinary collaboration between computer scientists and social scientists to develop more robust and theoretically grounded approaches in emotion mining. By addressing these gaps, we aim to enhance the effectiveness and reliability of emotion mining methodologies, ultimately contributing to improved disaster preparedness, response, and recovery. Keywords: emotion mining, sentiment analysis, natural disasters, psychology, technological disasters

Natural disasters act as a serious threat globally, requiring effective and efficient disaster management and recovery. This paper focuses on classifying natural disaster images using Convolutional Neural Networks (CNNs). Multiple CNN architectures were built and trained on a dataset containing images of earthquakes, floods, wildfires, and volcanoes. A stacked CNN ensemble approach proved to be the most effective, achieving 95% accuracy and an F1 score going up to 0.96 for individual classes. Tuning hyperparameters of individual models for optimization was critical to maximize the models' performance. The stacking of CNNs with XGBoost acting as the meta-model utilizes the strengths of the CNN and ResNet models to improve the overall accuracy of the classification. Results obtained from the models illustrated the potency of CNN-based models for automated disaster image classification. This lays the foundation for expanding these techniques to build robust systems for disaster response, damage assessment, and recovery management.

Haywood Talcove, the CEO of LexisNexis Risk Solutions' government division, told Fox News Digital that he believes there will be more than $1 trillion in artificial intelligence-assisted fraud if U.S. doesn't act quickly. America's spending on artifical intelligence in public safety is projected to increase from $9.3 billion in 2022 to $71 billion by 2030, according to a new analysis by the Insight Partners research firm. The projected seven-year boom is expected to be fueled by global and domestic terrorism, a growing need for security training and rising public safety demands coming out of the pandemic, the study says. "From emergency response to disaster prevention, AI has the potential to revolutionize the way we manage crises and protect our communities," according to the study. "Through advanced algorithms, machine learning and predictive analytics, AI can help first responders and public safety officials make more informed decisions, respond more quickly to emergencies and even prevent disasters from occurring in the first place."

Cal Fire and other agencies use a network of over 1,000 cameras statewide to track wildfires before, during and after. The public can now access the network, too. Wildfire season is almost here, and there's a new way you can help firefighters -- from anywhere. The University of California San Diego and state fire agencies have partnered to launch a public website for people to watch live camera feeds across the state. The program called ALERTCalifornia also helps firefighters fight fires by using a network of more than 1,000 live camera sensors to track the fires before, during and after.