دانلود مقاله انگلیسی رایگان:شناسایی مبتنی بر هوش مصنوعی از حوضه های جریان باقی مانده با فرکانس پایین در حوضه رودخانه Bailong ، چین - 2020
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  • AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China
    AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China


    ترجمه فارسی عنوان مقاله:

    شناسایی مبتنی بر هوش مصنوعی از حوضه های جریان باقی مانده با فرکانس پایین در حوضه رودخانه Bailong ، چین


    منبع:

    Sciencedirect - Elsevier - Geomorphology 359 (2020) 107125


    نویسنده:

    Yan Zhao a, Xingmin Meng a,b,c,⁎, Tianjun Qi a, Feng Qinga,d, Muqi Xiong a, Yajun Li c, Peng Guoc, Guan Chen c


    چکیده انگلیسی:

    Debris flow is a major geohazard inmountainous regions and poses a significant threat to life and property. The damage caused by debris flows have increased with the expansion of human settlements and activity into the mountainous regions of China. In regards to risks from debris flows, previously unrecognized low-frequency debris flow catchments constitute an especially significant threat. According to our investigation, only about 500 catchments have debris flow records in N2000 catchments of Bailong River basin. The main purpose of this paper is to introduce a new methodology using Artificial Intelligence (AI) that can simultaneously input parameters related to geomorphological conditions andmaterial conditions to better distinguish low-frequency debris flow catchments (LFDs) frommedium-high frequency debris flow catchments (MHFDs). A total of 449 prototypical debris flow catchments, 15 parameters, and 9 commonly used learning machines were used to build identification models. Debris flow catchments are divided into 4 cases (LO1-LO4) based on different sample ratios of LFDs and MHFDs, which are input into each classifier one by one. Based on model evaluation, the CHAID model in the case LO2 performs best, which only uses five parameters (formation lithology index, land use index, vegetation coverage index, drainage density and landslide density index) to predict LFDs. The results indicate that LFDs are mainly distributed in areas with less landslide distribution and better vegetation coverage compared with MHFDs. However, the distribution of LFDs is concentrated on FLI (formation lithology index) = 4, which is the weak lithology area. The tree classifier seems to be better at classifying fluvial processes. The model developed in this paper can help us quickly find LFDs in similar areas, and help to assess the risk of debris flows.
    Keywords: Low-frequency debris flow | Artificial Intelligence | Classification machine | Bailong River basin, China


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 15
    حجم فایل: 5689 کیلوبایت

    قیمت: رایگان


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