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تحلیل الگوی پراکنش خاکها در یک منطقه هموار با استفاده از الگوریتم درخت تصمیمگیری | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 160، دوره 50، شماره 2، خرداد و تیر 1398، صفحه 463-480 اصل مقاله (1.87 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2018.250737.667837 | ||
| نویسندگان | ||
| مرضیه زندی باغچه مریم* 1؛ پرویز شکاری2 | ||
| 1گروه علوم و مهندسی خاک-دانشکده کشاورزی-دانشگاه رازی-کرمانشاه-ایران | ||
| 2گروه 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 | ||
| نقشهبرداری رقومی خاک؛ الگوریتم C5.0؛ بوستینگ؛ متغیرهای محیطی؛ دشت میاندربند | ||
| عنوان مقاله [English] | ||
| Soil Distribution Pattern Analysis in a Low Relief Area Using Decision Trees Algorithm | ||
| نویسندگان [English] | ||
| marziye zandi baghche-maryam1؛ parviz shekaari2 | ||
| 1Department of Soil Science and Engineering-Faculty of Agriculture -RAZI UNIVERCITY-KERMANSHAH-IRAN | ||
| 2Department 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 | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 304 تعداد دریافت فایل اصل مقاله: 361 |
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