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Japan Today
7 days ago
- Climate
- Japan Today
Landslide-prone Nepal tests AI-powered warning system
Nepal is especially vulnerable due to unstable geology, shifting rainfall patterns and poorly planned development By Anup OJHA Every morning, Nepali primary school teacher Bina Tamang steps outside her home and checks the rain gauge, part of an early warning system in one of the world's most landslide-prone regions. Tamang contributes to an AI-powered early warning system that uses rainfall and ground movement data, local observations and satellite imagery to predict landslides up to weeks in advance, according to its developers at the University of Melbourne. From her home in Kimtang village in the hills of northwest Nepal, 29-year-old Tamang sends photos of the water level to experts in the capital Kathmandu, a five-hour drive to the south. "Our village is located in difficult terrain, and landslides are frequent here, like many villages in Nepal," Tamang told AFP. Every year during the monsoon season, floods and landslides wreak havoc across South Asia, killing hundreds of people. Nepal is especially vulnerable due to unstable geology, shifting rainfall patterns and poorly planned development. As a mountainous country, it is already "highly prone" to landslides, said Rajendra Sharma, an early warning expert at the National Disaster Risk Reduction and Management Authority. "And climate change is fuelling them further. Shifting rainfall patterns, rain instead of snowfall in high altitudes and even increase in wildfires are triggering soil erosion," Sharma told AFP. Landslides killed more than 300 people last year and were responsible for 70 percent of monsoon-linked deaths, government data shows. Tamang knows the risks first hand. When she was just five years old, her family and dozens of others relocated after soil erosion threatened their village homes. They moved about a kilometer uphill, but a strong 2015 earthquake left the area even more unstable, prompting many families to flee again. "The villagers here have lived in fear," Tamang said. "But I am hopeful that this new early warning system will help save lives." The landslide forecasting platform was developed by Australian professor Antoinette Tordesillas with partners in Nepal, Britain and Italy. Its name, SAFE-RISCCS, is an acronym of a complex title -- Spatiotemporal Analytics, Forecasting and Estimation of Risks from Climate Change Systems. "This is a low-cost but high-impact solution, one that's both scientifically informed and locally owned," Tordesillas told AFP. Professor Basanta Adhikari from Nepal's Tribhuvan University, who is involved in the project, said that similar systems were already in use in several other countries, including the United States and China. "We are monitoring landslide-prone areas using the same principles that have been applied abroad, adapted to Nepal's terrain," he told AFP. "If the system performs well during this monsoon season, we can be confident that it will work in Nepal as well, despite the country's complex Himalayan terrain." In Nepal, it is being piloted in two high-risk areas: Kimtang in Nuwakot district and Jyotinagar in Dhading district. Tamang's data is handled by technical advisers like Sanjaya Devkota, who compares it against a threshold that might indicate a landslide. "We are still in a preliminary stage, but once we have a long dataset, the AI component will automatically generate a graphical view and alert us based on the rainfall forecast," Devkota said. "Then we report to the community, that's our plan." The experts have been collecting data for two months, but will need a data set spanning a year or two for proper forecasting, he added. Eventually, the system will deliver a continuously updated landslide risk map, helping decision makers and residents take preventive actions and make evacuation plans. The system "need not be difficult or resource-intensive, especially when it builds on the community's deep local knowledge and active involvement", Tordesillas said. Asia suffered more climate and weather-related hazards than any other region in 2023, according to UN data, with floods and storms the most deadly and costly. And while two-thirds of the region have early warning systems for disasters in place, many other vulnerable countries have little coverage. In the last decade, Nepal has made progress on flood preparedness, installing 200 sirens along major rivers and actively involving communities in warning efforts. The system has helped reduce flooding deaths, said Binod Parajuli, a flood expert with the government's hydrology department. "However, we have not been able to do the same for landslides because predicting them is much more complicated," he said. "Such technologies are absolutely necessary if Nepal wants to reduce its monsoon toll." © 2025 AFP
Business Wire
06-08-2025
- Business
- Business Wire
Peraton Honored with ACCELERATE 2025 Award for Innovation in AI and Data
RESTON, Va.--(BUSINESS WIRE)--Peraton has been recognized in the prestigious ACCELERATE 2025 Awards for advancing federal innovation through its Center for Forecasting and Outbreak Analytics program and Rapid Fraud Intelligence (Rapid FI) solution. Hosted by GovTech Connects, the ACCELERATE Awards celebrate exceptional achievements in deploying AI, data, and digital solutions across federal, DoD, and industry sectors to drive mission success. 'Our solutions that are being recognized as best-in-class awards reflect our commitment to innovation and mission alignment—empowering agencies with the tools they need to act faster, smarter, and with greater confidence," said Tarik Reyes. Share 'Our team is deeply honored to be among the 2025 awardees,' said Tarik Reyes, president, Defense Mission & Health Solutions Sector, Peraton. 'Our solutions that are being recognized as best-in-class awards reflect our commitment to innovation and mission alignment—empowering agencies with the tools they need to act faster, smarter, and with greater confidence. We're proud to support our federal partners with adaptive technologies that scale with their needs.' Peraton's Rapid FI platform stood out for its impact in transforming fraud detection and response within government programs. Designed with a modular architecture, Rapid FI is highly scalable and easily configurable, enabling federal agencies to tailor it uniquely to their mission requirements—whether combating benefit fraud, streamlining audit processes, or enhancing real-time risk analysis. To view the full list of ACCELERATE 2025 Award winners, visit:
Observer
01-08-2025
- Climate
- Observer
Landslide-prone Nepal tests AI-powered warning system
Every morning, Nepali primary school teacher Bina Tamang steps outside her home and checks the rain gauge, part of an early warning system in one of the world's most landslide-prone regions. Tamang contributes to an AI-powered early warning system that uses rainfall and ground movement data, local observations and satellite imagery to predict landslides up to weeks in advance, according to its developers at the University of Melbourne. From her home in Kimtang village in the hills of northwest Nepal, 29-year-old Tamang sends photos of the water level to experts in the capital Kathmandu, a five-hour drive to the south. "Our village is located in difficult terrain and landslides are frequent here, like many villages in Nepal", Tamang said. Every year during the monsoon season, floods and landslides wreak havoc across South Asia, killing hundreds of people. Nepal is especially vulnerable due to unstable geology, shifting rainfall patterns and poorly planned development. A damaged building, uprooted following heavy rains at a landslide-affected village in Lalitpur district on the outskirts of Kathmandu. — AFP As a mountainous country, it is already "highly prone" to landslides, said Rajendra Sharma, an early warning expert at the National Disaster Risk Reduction and Management Authority. "And climate change is fuelling them further. Shifting rainfall patterns, rain instead of snowfall in high altitudes and even increase in wildfires are triggering soil erosion", Sharma said. Landslides killed more than 300 people last year and were responsible for 70 per cent of monsoon-linked deaths, government data shows. Tamang knows the risks first hand. When she was just five years old, her family and dozens of others relocated after soil erosion threatened their village homes. They moved about a kilometre uphill, but a strong 2015 earthquake left the area even more unstable, prompting many families to flee again. "The villagers here have lived in fear", Tamang said. "But I am hopeful that this new early warning system will help save lives". The landslide forecasting platform was developed by Australian professor Antoinette Tordesillas with partners in Nepal, Britain and Italy. Its name, SAFE-RISCCS, is an acronym of a complex title — Spatiotemporal Analytics, Forecasting and Estimation of Risks from Climate Change Systems. "This is a low-cost but high-impact solution, one that's both scientifically informed and locally owned", Tordesillas said. Prof Basanta Adhikari from Nepal's Tribhuvan University, who is involved in the project, said that similar systems were already in use in several other countries, including the US and China. "We are monitoring landslide-prone areas using the same principles that have been applied abroad, adapted to Nepal's terrain", he said. "If the system performs well during this monsoon season, we can be confident that it will work in Nepal as well, despite the country's complex Himalayan terrain". In Nepal, it is being piloted in two high-risk areas: Kimtang in Nuwakot district and Jyotinagar in Dhading district. Tamang's data is handled by technical advisers like Sanjaya Devkota, who compares it against a threshold that might indicate a landslide. Rajendra Sharma, Disaster Risk Manager, National Disaster Risk Reduction and Management Authority (NDRRMA) "We are still in a preliminary stage, but once we have a long dataset, the AI component will automatically generate a graphical view and alert us based on the rainfall forecast", Devkota said. "Then we report to the community, that's our plan". The experts have been collecting data for two months, but will need a data set spanning a year or two for proper forecasting, he added. Eventually, the system will deliver a continuously updated landslide risk map, helping decision makers and residents take preventive actions and make evacuation plans. The system "need not be difficult or resource-intensive, especially when it builds on the community's deep local knowledge and active involvement", Tordesillas said. — AFP
Straits Times
01-08-2025
- Climate
- Straits Times
Landslide-prone Nepal tests AI-powered warning system
Sign up now: Get ST's newsletters delivered to your inbox Nepal is especially vulnerable to landslides due to unstable geology, shifting rainfall patterns and poorly planned development. Kathmandu - Every morning, Nepali primary school teacher Bina Tamang steps outside her home and checks the rain gauge, part of an early warning system in one of the world's most landslide-prone regions. She contributes to an AI-powered early warning system that uses rainfall and ground movement data, local observations and satellite imagery to predict landslides up to weeks in advance, according to its developers at the University of Melbourne. From her home in Kimtang village in the hills of northwest Nepal, 29-year-old Tamang sends photos of the water level to experts in the capital Kathmandu, a five-hour drive to the south. 'Our village is located in difficult terrain, and landslides are frequent here, like many villages in Nepal,' Ms Tamang told AFP. Every year during the monsoon season, floods and landslides wreak havoc across South Asia, killing hundreds of people. Nepal is especially vulnerable due to unstable geology, shifting rainfall patterns and poorly planned development. As a mountainous country, it is already 'highly prone' to landslides, said Mr Rajendra Sharma, an early warning expert at the National Disaster Risk Reduction and Management Authority. 'And climate change is fuelling them further. Shifting rainfall patterns, rain instead of snowfall in high altitudes and even increase in wildfires are triggering soil erosion,' he told AFP. Saving lives Landslides killed more than 300 people in 2024 and were responsible for 70 per cent of monsoon-linked deaths, government data shows. Ms Tamang knows the risks first hand. When she was just five years old, her family and dozens of others relocated after soil erosion threatened their village homes. They moved about 1km uphill, but a strong 2015 earthquake left the area even more unstable, prompting many families to flee again. 'The villagers here have lived in fear,' Ms Tamang said. 'But I am hopeful that this new early warning system will help save lives.' The landslide forecasting platform was developed by Australian professor Antoinette Tordesillas with partners in Nepal, Britain and Italy. Its name, SAFE-RISCCS, is an acronym of a complex title – Spatiotemporal Analytics, Forecasting and Estimation of Risks from Climate Change Systems. 'This is a low-cost but high-impact solution, one that's both scientifically informed and locally owned,' Prof Tordesillas told AFP. Professor Basanta Adhikari from Nepal's Tribhuvan University, who is involved in the project, said that similar systems were already in use in several other countries, including the United States and China. 'We are monitoring landslide-prone areas using the same principles that have been applied abroad, adapted to Nepal's terrain,' he told AFP. 'If the system performs well during this monsoon season, we can be confident that it will work in Nepal as well, despite the country's complex Himalayan terrain.' In Nepal, it is being piloted in two high-risk areas: Kimtang in Nuwakot district and Jyotinagar in Dhading district. Early warnings Ms Tamang's data is handled by technical advisers like Mr Sanjaya Devkota, who compares it against a threshold that might indicate a landslide. 'We are still in a preliminary stage, but once we have a long dataset, the AI component will automatically generate a graphical view and alert us based on the rainfall forecast,' Mr Devkota said. 'Then we report to the community, that's our plan.' The experts have been collecting data for two months, but will need a data set spanning a year or two for proper forecasting, he added. Eventually, the system will deliver a continuously updated landslide risk map, helping decision makers and residents take preventive actions and make evacuation plans. The system 'need not be difficult or resource-intensive, especially when it builds on the community's deep local knowledge and active involvement', Prof Tordesillas said. Asia suffered more climate and weather-related hazards than any other region in 2023, according to UN data, with floods and storms the most deadly and costly. And while two-thirds of the region have early warning systems for disasters in place, many other vulnerable countries have little coverage. In the last decade, Nepal has made progress on flood preparedness, installing 200 sirens along major rivers and actively involving communities in warning efforts. The system has helped reduce flooding deaths, said Mr Binod Parajuli, a flood expert with the government's hydrology department. 'However, we have not been able to do the same for landslides because predicting them is much more complicated,' he said. 'Such technologies are absolutely necessary if Nepal wants to reduce its monsoon toll.' AFP
Forbes
16-07-2025
- Business
- Forbes
The Future Of Forecasting: How AI Can Help Industries Predict Demand For Products That Don't Exist Yet
Dileep Kumar Rai is a Global Supply Chain Optimization Expert, Oracle Fusion Cloud architect, and demand forecasting leader. Launching a new product in today's market isn't just risky; it's like setting sail into uncharted waters during a storm, with no compass and relying solely on your instincts to guide you. Throughout my years of working alongside leaders in the automotive, tech, fashion, FMCG and pharmaceutical industries, I've uncovered a widespread yet underappreciated challenge: the 'no-data' dilemma. When introducing something genuinely new to the market, the usual tools—such as ERP forecasts, historical sales curves and even seasoned intuition—often fall short. The result? Overproduction, lost revenue or empty shelves. My Industry Finding: The 'No-Data' Dilemma Through extensive research and direct collaboration with industry innovators, I have identified four recurring pain points that keep executives awake at night: This isn't just an abstract problem; it's a daily operational reality that costs companies millions in missed opportunities and excess inventory. The Cross-Industry AI Forecasting Framework To address this, I developed a proprietary and adaptable AI/ML-powered forecasting framework that combines the art of human judgment with the science of machine learning, tailoring it to each industry's unique dynamics. The Secret Sauce: Understanding The Framework Consider launching a new product as similar to crafting a new recipe: • Data Ingestion: My 'pantry' is stocked with both internal ERP staples and the freshest external ingredients: social buzz, competitor actions, and economic factors signals. • Feature Engineering: Here's the spice rack mixing predictors like launch buzz, product attributes and campaign reach to create a distinctive flavor profile for every launch. • Tailored ML Models: These reflect the cooking techniques of XG-Boost for FMCG, LSTM (long short-term memory) for fashion, and Bayesian models for pharma, each adapted to the industry's unique texture and volatility. • Scenario Simulator: This is my test kitchen, where I experiment with different launch 'recipes,' adjusting prices, channels and competitor responses to determine which flavors succeed. • Executive Dashboard: The tasting table provides real-time insights, confidence intervals and scenario comparisons, empowering leaders to select the best options before introducing them to the market. Industry-Specific Flavor Profiles No two industries have the same palate. This framework adapts like a master chef: Key Drivers: Seasonality, social buzz Modeling Approach: LSTM combined with NLP to capture fast-changing trends and consumer sentiment. Key Drivers: Economic cycles, supply chain lag Modeling Approach: DeepAR paired with time series boosting to handle production volatility and macroeconomic shifts. Key Drivers: Retail promotions, regional preferences Modeling Approach: XGBoost with promotion uplift models to track short-term promotional lifts and geographic variability. Key Drivers: Clinical trial data, prescriber behavior Modeling Approach: Bayesian Models combined with ARIMAX to incorporate clinical data uncertainty and prescribing trends. The Unique Ingredient: Comp Chaining For New Product Launches A cornerstone of my framework is a technique I call Comp Chaining. This method enables us to 'borrow' the sales history of analogous products and blend it with new launch scenarios, even when there's no direct precedent. Here's how Comp Chaining works: • Choose A New Product: Select a target new product and a source-comparable (Comp) product. • Align Sales Histories: Shift the company's sales timeline to match the new launch, week by week. • Adjust For Differences: Use ratios and statistical adjustments to account for differences in initial orders, program length or market conditions. • Blend With AI Forecasts: Where the Comp's history runs out, machine learning fills in the gaps, seasoning the forecast with real-time external data. • Simulate And Refine: Run multiple scenarios, adjusting the blend until the forecast matches the unique profile of your new product. This approach doesn't just copy the past; it creates a new recipe, tailored for today's market and tomorrow's challenges. How To Bring This Framework To Your Organization Final Thoughts: Turning Uncertainty Into A Strategic Ingredient Forecasting demand for new products was once a tedious and uncertain game of guesswork. This framework demonstrates that it doesn't have to be that way. By blending real-world data, AI and Comp Chaining, you can turn uncertainty from a risk into your secret ingredient for growth. In a world where innovation cycles are accelerating and consumer attention is fleeting, the question isn't whether you can afford to add this flavor to your forecasting. The real question is: Can you afford not to? Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
