Latest news with #SupportVectorMachine
Time of India
20-05-2025
- Science
- Time of India
Himalayan river basins face escalating landslide threats, Jhelum most vulnerable, warns study
Dehradun: A comprehensive new study, led by researchers from Aligarh Muslim University (AMU), Jamia Millia Islamia, and King Saud University, reveals that vast areas of the Himalayan river basins are under serious threat from landslides, posing grave risks to both lives and livelihoods. The study identified the Jhelum river basin as the most vulnerable, with nearly 900,000 hectares of agricultural land and over 37,000 hectares of built-up areas at risk. The Kali and Ganga river basins were also found to be high-risk zones, with more than 287,000 hectares and 140,000 hectares of agricultural land exposed to potential landslides respectively. Published in the journal 'All Earth', the research was conducted using advanced machine learning techniques combined with geospatial data to map highly vulnerable zones across the region. Researchers used Support Vector Machine (SVM) models to predict landslide susceptibility based on a range of natural variables, including terrain, rainfall, vegetation cover, and forest fire activity. Led by AMU's Zainab Khan, the team also factored in population density, land use, and terrain features for a comprehensive risk assessment. "Policymakers now have a map of risk. What they need next is a roadmap of response," said Khan. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Giao dịch Bitcoin và Ethereum - Không cần ví! IC Markets BẮT ĐẦU NGAY Undo "This study not only identifies where the risks are greatest, but also why. That makes it a vital tool for proactive governance." In the Jhelum basin alone, over 2.3 million people are living in areas classified as "high" or "very high" risk. Similar patterns were observed across the Indus, Yamuna, and Ganga basins, highlighting the widespread nature of the threat, as per the study. Using a method called SHAP (Shapley Additive Explanations), the study identified runoff, forest fires, the number of nearby streams, and stream power index, which is a measure of the erosive power of flowing water, as the biggest contributors to landslide risk. These were followed by factors like steep slopes, how wet the ground tends to stay, and vegetation health and density. While factors like elevation and geology played a relatively minor role, the study emphasised the significant impact of forest fires on slope stability. Fires not only destroy the vegetation that binds the soil but also create a water-repellent layer that increases runoff, accelerating erosion and slope failure. The impact of landslides on infrastructure and agriculture are catastrophic as it can block roads, damage crops, and bury homes, hampering both local economies and emergency response efforts. In densely populated valleys and foothill zones, even moderate-risk areas can suffer devastating losses due to the concentration of people and assets. For instance, the Yamuna river basin has over 100,000 hectares of vulnerable agricultural land and more than 10,000 hectares of urban settlements at risk. The Sutlej and Giri basins also face high landslide risk, with thousands of hectares of land and tens of thousands of residents exposed to potential danger. The researchers recommend targeted mitigation measures, including afforestation, slope stabilisation, and restrictions on construction in high-risk areas. They stress that the integration of early warning systems and community-based disaster preparedness is critical to reducing the toll of landslides in the future.
Yahoo
05-05-2025
- Science
- Yahoo
AI Detects an Unusual Detail Hidden in a Famous Raphael Masterpiece
Artificial intelligence (AI) can be trained to see details in images that escape the human eye, and an AI neural network identified something unusual about a face in a Raphael painting: It wasn't actually painted by Raphael. The face in question belongs to St Joseph, seen in the top left of the painting known as the Madonna della Rosa (or Madonna of the Rose). Scholars have in fact long debated whether or not the painting is a Raphael original. While it requires diverse evidence to conclude an artwork's provenance, a newer method of analysis based on an AI algorithm has sided with those who think at least some of the strokes were at the hand of another artist. Researchers from the UK and US developed a custom analysis algorithm based on the works that we know are the result of the Italian master's brushwork. "Using deep feature analysis, we used pictures of authenticated Raphael paintings to train the computer to recognize his style to a very detailed degree, from the brushstrokes, the color palette, the shading and every aspect of the work," mathematician and computer scientist Hassan Ugail from the University of Bradford in the UK explained in 2023, when the researchers' findings were published. "The computer sees far more deeply than the human eye, to microscopic level. " Machine learning processes typically need to be trained on a vast pool of examples, something which isn't always available when it comes to a sole artist's life work. In this case, the team modified pre-trained architecture developed by Microscoft called ResNet50, coupled with a traditional machine learning technique called a Support Vector Machine. The method has previously been shown to have a 98 percent accuracy level when it comes to identifying Raphael paintings. Usually, it's trained on whole pictures, but here the team also asked it to look at individual faces. While the Madonna, the Child, and St John all show up as being created from the hand of Raphael, that's not the case of St Joseph. The researchers note that in previous debates over the painting's authenticity, St Joseph's face has been thought to be less well done than the others in the frame. "When we tested the della Rosa as a whole, the results were not conclusive," said Ugail. "So, then we tested the individual parts and while the rest of the picture was confirmed as Raphael, Joseph's face came up as most likely not Raphael." Giulio Romano, one of Raphael's pupils, may have been responsible for the fourth face, but that's by no means certain. It's another example of modern technology revealing the secrets of classic paintings – this time with AI. The Madonna della Rosa was painted on canvas in the years 1518 to 1520, experts think. It was in the mid-1800s that art critics started to suspect that Raphael might not have painted all of the artwork. Now those suspicions have almost certainly been proved correct, though the research team behind the study is keen to emphasize that this AI will be helping out art experts in the future, rather than replacing them. "This is not a case of AI taking people's jobs," said Ugail. "The process of authenticating a work of art involves looking at many aspects, from its provenance, pigments, condition of the work and so on. "However, this sort of software can be used as one tool to assist in the process." The research was published in Heritage Science. An earlier version of this article was published in December 2023. Scientists Don't Know Why Consciousness Exists, And a New Study Proves It Men Tend to Fall in Love Faster Than Women, New Study Shows Legendary Female Free-Divers Reveal Evolution in Action on South Korean Island
