کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6426572 1634442 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran
ترجمه فارسی عنوان
مقایسه شبکه های عصبی مصنوعی و مدل های درخت تصمیم گیری در برآورد توزیع فضایی عمق برف در یک منطقه نیمه خشک ایران
کلمات کلیدی
عمق برف، کوبیست، درخت تصمیم گیری، پارامترهای زمین شبکه های عصبی مصنوعی،
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علوم زمین و سیاره ای (عمومی)
چکیده انگلیسی


- Snow depth was digitally mapped at a fine resolution in a semi-arid region of Iran.
- Two common algorithms were compared.
- Different source of ancillary data was applied.

There is no doubt that snow cover plays an important role in the hydrological cycle of mountainous basins. Therefore, it is essential to measure snow parameters such as snow depth and snow water equivalent in these areas. The aim of this study is to estimate the snow depth from terrain parameters in the Sakhvid Basin, Iran using artificial neural networks (ANNs) and M5 algorithm of decision tree. For this purpose, snow depths were measured in 206 sites based on systematic network. Furthermore, 30 terrain parameters were extracted from a digital elevation model (DEM) of the basin. The results indicated that the decision tree model is the most suitable method to estimate snow depth in the study area with a Nash-Sutcliffe Efficiency (Ens) of 0.80, followed by ANNs with an Ens of 0.73. Moreover, the most significant parameters in the M5 decision tree algorithm are: channel network base level, stream power, wetness index and height.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Cold Regions Science and Technology - Volume 122, February 2016, Pages 26-35
نویسندگان
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