تحلیل الگوی پراکنش خاک‌ها در یک منطقه هموار با استفاده از الگوریتم درخت تصمیم‌گیری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی خاک-دانشکده کشاورزی-دانشگاه رازی-کرمانشاه-ایران

2 گروه علوم ومهندسی خاک، دانشکده کشاورزی،دانشگاه رازی، کرمانشاه، ایران

چکیده

نقشه‌برداری رقومی خاک را می­توان تولید اطلاعات مکانی خاک تعریف کرد. یکی از روش­های محبوب که اخیراً در چندین مورد از مطالعات نقشه‌برداری رقومی ‌خاک به‌کاررفته، درخت تصمیم­گیری است. پژوهش حاضر به‌منظور ارزیابی قابلیت درخت تصمیم­گیری در نقشه­برداری خاک­ها در منطقه میان­دربند با مساحت50000 هکتار در استان کرمانشاه انجام شد. الگوریتم C5.0 (با و بدون متاالگوریتم بوستینگ) برای ایجاد روابط مکانی بین کلاس­های خاک و متغیرهای محیطی مورد استفاده قرار گرفت. بر پایه نمونه­برداری سیستماتیک 78 خاکرخ مورد مطالعه قرار گرفت و 6 گروه بزرگ و 14 زیرگروه شناسایی شد. 30 متغیر محیطی از مدل رقومی ارتفاع و تصویر سنجنده OLI/TIRS ماهواره لندست 8 مربوط به تاریخ تیرماه 1394 مشتق شد. صحت عمومی برای گروه بزرگ و زیرگروه برابر با 73 درصد به دست آمد درحالی‌که مقادیر متناظر برای نمایه کاپا به ترتیب 61/0 و 63/0 بود. ترکیب متاالگوریتم بوستینگ با C5.0 مقادیر صحت عمومی را به ترتیب به 80 درصد و 76 درصد و مقادیر نمایه کاپا را به 72/0 و 66/0 افزایش داد. نتایج توانایی قابل‌توجهی را برای درخت تصمیم‌گیری در باز شناخت الگوی خاک در منطقه موردمطالعه نشان داد و متغیرهای توپوگرافی از سایر متغیرهای محیطی پر اهمیت­تر به نظر می‌رسید. همچنین، بررسی نقشه­های تولیدشده از طریق مقایسه با الگوی خاک مشاهده‌شده در خلال بررسی زمین، نشان‌گر تطابق پذیرفتنی پیش‌بینی­های الگوریتم درخت تصمیم‌گیری با واقعیت بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Soil Distribution Pattern Analysis in a Low Relief Area Using Decision Trees Algorithm

نویسندگان [English]

  • marziye zandi baghche-maryam 1
  • parviz shekaari 2
1 Department of Soil Science and Engineering-Faculty of Agriculture -RAZI UNIVERCITY-KERMANSHAH-IRAN
2 Department of Soil Science and Engineering, Faculty of Agriculture , RAZI Univercity, Kermanshah, Iran
چکیده [English]

Digital soil mapping (DSM) can be defined as a production of spatial soil information. Decision tree (DT) algorithm is one of the most popular machine learning methods which was applied in several recent DSM studies. This study was carried out to evaluate the capability of DT in mapping soils in Miandarband region with area of 50,000 ha in Kermanshah province. The C5.0 decision tree algorithm (with and without boosting meta-algorithm) used to establish spatial relationships between known soil taxonomic classes and environmental variables. Using simple systematic sampling, 78 pedons were studied and 6 great groups and 14 subgroups of Soil Taxonomy (ST) were identified. Thirty environmental items were derived from a digital elevation model (DEM) file and a landsat-8 OLI/TIRS (July/Tir 1394) image of the area. Predictions made by C5.0 algorithm showed OA values of 73 percent for great group and subgroup, while comparable values for Kappa Index were 0.61 and 0.63, respectively. Combination of boosting meta-algorithm with C5.0 increased OA values for ST categories 0.80 and 0.76 and Kappa Index values to 72 percent and 66 percent. Results showed a considerable capability for DT in recognition of soil pattern over the study area and the topographic variables seems to be most important. Also, analysis of the produced maps, compared with the observed soil pattern during the field survey, revealed a reasonable agreement of decision tree algorithm predictions with reality.

کلیدواژه‌ها [English]

  • : Digital Soil Mapping
  • C5.0 algorithm
  • Boosting
  • Environmental Covariates
  • Miandaband Plain
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