首页|Data from Department of Civil Engineering Provide New Insights into Machine Lear ning (Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning)
Data from Department of Civil Engineering Provide New Insights into Machine Lear ning (Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting out of the Department of Civil En gineering by NewsRx editors, research stated, "Landslides are devastating natura l disasters that generally occur on fragile slopes. Landslides are influenced by many factors, such as geology, topography, natural drainage, land cover, rainfa ll and earthquakes, although the underlying mechanism is too complex and very di fficult to explain in detail." Our news journalists obtained a quote from the research from Department of Civil Engineering: "In this study, the susceptibility mapping of co-seismic landslide s is carried out using a machine learning approach, considering six districts co vering an area of 12,887 km2 in Nepal. Landslide inventory map is prepared by ta king 23,164 post seismic landslide data points that occurred after the 7.8 MW 20 15 Gorkha earthquake. Twelve causative factors, including distance from the rupt ure plane, peak ground acceleration and distance from the fault, are considered input parameters. The overall accuracy of the model is 87.2%, the a rea under the ROC curve is 0.94, the Kappa coefficient is 0.744 and the RMSE val ue is 0.358, which indicates that the performance of the model is excellent with the causative factors considered. The susceptibility thus developed shows that Sindhupalchowk district has the largest percentage of area under high and very h igh susceptibility classes, and the most susceptible local unit in Sindhupalchow k is the Barhabise municipality, with 19.98% and 20.34% of its area under high and very high susceptibility classes, respectively. For t he analysis of building exposure to co-seismic landslide susceptibility, a build ing footprint map is developed and overlaid on the co-seismic landslide suscepti bility map. The results show that the Sindhupalchowk and Dhading districts have the largest and smallest number of houses exposed to co-seismic landslide suscep tibility."
Department of Civil EngineeringCyborgsEmerging TechnologiesMachine Learning
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