Things to Do in L.A. Tap to enable a layout that focuses on the article. Commuters wait for a train against dark skies at the MTA's Expo/Bundy station in Culver City on Thursday. An unseasonable shift in weather is bringing the chance of showers and thunderstorms across Southern California, prompting some concerns about flooding as temperatures also drop well below average for mid-September. In much of the Los Angeles area, the system is expected to bring only light rain or drizzling Thursday and Friday, but there is a possibility for pockets of thunderstorms that could bring heavier rain. The greatest chance for thunderstorms is in the mountains, including along the Interstate 5 corridor and across the San Gabriels, according to Bryan Lewis, a National Weather Service meteorologist in Oxnard. "We're looking at mostly less than a tenth of an inch, maybe up to a quarter of an inch in the mountains," Lewis said.

Obtaining high-resolution, accurate channel topography and deposit conditions is the prior challenge for the study of channelized debris flow. Currently, wide-used mapping technologies including satellite imaging and drone photogrammetry struggle to precisely observe channel interior conditions of mountainous long-deep gullies, particularly those in the Wenchuan Earthquake region. SLAM is an emerging tech for 3D mapping; however, extremely rugged environment in long-deep gullies poses two major challenges even for the state-of-art SLAM: (1) Atypical features; (2) Violent swaying and oscillation of sensors. These issues result in large deviation and lots of noise for SLAM results. To improve SLAM mapping in such environments, we propose an advanced SLAM-based channel detection and mapping system, namely AscDAMs. It features three main enhancements to post-process SLAM results: (1) The digital orthophoto map aided deviation correction algorithm greatly eliminates the systematic error; (2) The point cloud smoothing algorithm substantially diminishes noises; (3) The cross section extraction algorithm enables the quantitative assessment of channel deposits and their changes. Two field experiments were conducted in Chutou Gully, Wenchuan County in China in February and November 2023, representing observations before and after the rainy season. We demonstrate the capability of AscDAMs to greatly improve SLAM results, promoting SLAM for mapping the specially challenging environment. The proposed method compensates for the insufficiencies of existing technologies in detecting debris flow channel interiors including detailed channel morphology, erosion patterns, deposit distinction, volume estimation and change detection. It serves to enhance the study of full-scale debris flow mechanisms, long-term post-seismic evolution, and hazard assessment.

Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfall, leads to alerts when the measure exceeds a predefined threshold. However, this system generates many false alarms and misses a substantial fraction of the actual debris flows. Towards improving this system, we implemented five machine learning models that input historical rainfall data and predict whether a debris flow will occur within a selected time. We found that a random forest model performed the best among the five models and outperformed the existing system in Taiwan. Furthermore, we identified the rainfall trajectories strongly related to debris flow occurrences and explored trade-offs between the risks of missing debris flows versus frequent false alerts. These results suggest the potential for machine learning models trained on hourly rainfall data alone to save lives while reducing false alerts.

Machine learning could provide up an extra hour of warning time for debris flows along the Illgraben torrent in Switzerland, researchers report at the Seismological Society of America (SSA)'s 2021 Annual Meeting. Debris flows are mixtures of water, sediment and rock that move rapidly down steep hills, triggered by heavy precipitation and often containing tens of thousands of cubic meters of material. Their destructive potential makes it important to have monitoring and warning systems in place to protect nearby people and infrastructure. In her presentation at SSA, Ma?gorzata Chmiel of ETH Zürich described a machine learning approach to detecting and alerting against debris flows for the Illgraben torrent, a site in the European Alps that experiences significant debris flows and torrential events each year. Seismic records from stations located in the Illgraben catchment, from 20 previous debris flow events, were used to train an algorithm to recognize the seismic signals of debris flow formation, accurately detecting early flows 90% of the time. The machine learning system was able to detect all 13 debris flows and torrential events that occurred during a three-month period in 2020.

For applied scientists--that intrepid cadre who get their hands dirty in the sometimes dangerous world beyond the ivory tower, participating in difficult decisions with little time and major consequences--getting the right answer is only half the battle. The other half is successfully explaining what they've found, and what it means. This winter's debris flows in the posh community of Montecito, California, which led to more than 20 deaths, provided examples of success and failure on both counts. And those successes and failures have ramifications far beyond managing geophysical risks. Sean W. Fleming is a geophysicist by training and author of Where the River Flows: Scientific Reflections on Earth's Waterways.