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مدلسازی و پیشبینی مکانی کلاس خاک با استفاده از الگوریتم یادگیری رگرسیون درختی توسعهیافته و جنگل-های تصادفی در بخشی از اراضی دشت قزوین | ||
تحقیقات آب و خاک ایران | ||
مقاله 11، دوره 50، شماره 10، اسفند 1398، صفحه 2525-2538 اصل مقاله (1.66 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2019.280905.668198 | ||
نویسندگان | ||
سیدروح اله موسوی1؛ فریدون سرمدیان* 2؛ اصغر رحمانی3 | ||
1گروه علوم ومهندسی خاک، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، ایران | ||
2عضو هیأت علمی گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
3دانشجوی دکتری،گروه علوم ومهندسی خاک،پردیس کشاورزی ومنابع طبیعی، دانشگاه تهران | ||
چکیده | ||
انتخاب متغیرهای کمکی مناسب در روشهای یادگیرنده ماشینی جهت نقشهبرداری رقومی خاک از اهمیت ویژهای برخوردار است. طی سالهای اخیر در ایران استفاده از الگوریتمهای یادگیرنده در نقشهبرداری رقومی و بهنگام سازی نقشههای قدیمی توسعه یافته است. پژوهش حاضر در بخشی از اراضی دشت قزوین با هدف مقایسه جنگلهای تصادفی (RF) و رگرسیون درختی توسعهیافته (BRT) در پیشبینی مکانی کلاسهای زیرگروه و فامیل خاک بهمراه انتخاب متغیرهای کمکی با استفاده از شاخص تورم واریانس انجام شده است. 61 خاکرخ به روش نمونهبرداری تصادفی طبقهبندیشده حفر، تشریح و با تجزیهوتحلیل آزمایشگاهی تا سطح فامیل ردهبندی گردید. مناسبترین متغیرهای محیطی از میان 15 متغیر ژئومورفومتری و شاخصهای سنجش از دور با استفاده از فاکتور تورم واریانس انتخاب گردیدند. مدلسازی رابطه خاک – زمیننما در دو سطح زیرگروه و فامیل خاک با استفاده از دو الگوریتم یادگیرنده RF و BRT در نرمافزار RStudio بر اساس دو بسته "Randomforest" و "C5.0" اجرا گردید. نتایج انتخاب متغیرهای محیطی نشان داد که شش متغیر CHA،DEM ، STH، NDVI، SI و DVI بهعنوان متغیر ورودی انتخاب گردیدند. شاخصهای ارزیابی مدلها شامل صحت کلی و شاخص کاپا به ترتیب برای الگوریتم BRT، 35، 26 درصد و برای الگوریتم RF،70، 60 درصد در سطح فامیل خاک حاصل گردید. آنالیز حساسیت برمبنای شاخص میانگین حداقل صحت نشان داد که متغیر محیطی مساحت حوزه آبخیز اصلاحشده دارای بیشترین اهمیت نسبی در میان متغیرهای انتخاب شده است. بهطورکلی با استفاده از رویکردهای نوین انتخاب متغیر و الگوریتمهای یادگیرنده مؤثر میتوان نقشهی پراکنش مکانی خاکها را حتی در نواحی با پستیوبلندی کم با صحت قابلقبول تهیه نمود. | ||
کلیدواژهها | ||
نقشه برداری رقومی خاک؛ الگوریتم یادگیرنده؛ مدل جنگل تصادفی؛ درخت تصمیم توسعه یافته؛ دادهکاوی | ||
عنوان مقاله [English] | ||
Modelling and Prediction of Soil Classes Using Boosting Regression Tree and Random Forests Machine Learning Algorithms in Some Part of Qazvin Plain | ||
نویسندگان [English] | ||
Sayed Roholla Mousavi1؛ Fereydoon Sarmadian2؛ Asghar Rahmani3 | ||
1Science and Soil Engineering Department, Agriculture and Natural Resources faculty, University of Tehran, Iran. | ||
2soil science department< faculty of agricultural engineering and technology, university of Tehran | ||
3Ph.D Student of Soil Resources Management, Science and soil Engineering Department, Tehran university, | ||
چکیده [English] | ||
Appropriate selection of ancillary covariates have a specific important on digital soil mapping. Currently, use of machine learning algorithms for digital mapping and updating of conventional soil map has been developed in Iran. The current study has been done to compare the BRT and RF models for spatial prediction of subgroup and family classes with selection of axillary variables using VIF approach in some part of Qazvin Plain. 61 pedons were sampled based on stratified random, digged, described and classified with consideration of laboratory analysis up to family level. The most appropriate variables were selected among 15 Geomorphometry and Remote Sensing Indices using Variance Inflation Factor (VIF). Soil landscape modeling was conducted with RF and BRT learning algorithm in RStudio software based on Randomforest and C5.0 packages at subgroup and family levels. The results showed that six indices including CHA, DEM, STH, SI DVI and NDVI were selected as input variables. Assessment indices such as the Overall Accuracy (OA) and Kappa were obtained for BRT (35, 26%) and RF (70, 60%) at family level, respectively. Sensitivity analysis based on the mean decrease accuracy (MDA) revealed that the modified catchment area variable is the most relative important variable among the selected variables. Generally, by using feature selection innovative approach and effective learning algorithms, the spatial distribution of soil maps could be made even in low relief lands with acceptable accuracy. | ||
کلیدواژهها [English] | ||
digital soil mapping, Learning Algorithm, Random Forests Model, Boosting Decision Tree, Data Mining | ||
مراجع | ||
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