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نقشه‌برداری رقومی اجزا بافت خاک در بخشی از اراضی دشت خوزستان با استفاده از برخی مدل‌های یادگیری ماشین | ||
| تحقیقات آب و خاک ایران | ||
| دوره 53، شماره 10، دی 1401، صفحه 2261-2276 اصل مقاله (2.11 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2022.348442.669360 | ||
| نویسندگان | ||
| نسیم صحرایی1؛ احمد لندی2؛ سعید حجتی* 3 | ||
| 1گروه علوم و مهندسی خاک، دانشکده کشاورزی،دانشگاه شهید چمران اهواز، خوزستان، ایران | ||
| 2هیات علمی گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز- خوزستان، ایران | ||
| 3عضو هیئت علمی گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، خوزستان، ایران | ||
| چکیده | ||
| مطالعه حاضر با هدف ارزیابی و مقایسه کارائی مدل‌های ماشین بردار پشتیبان (SVM) و جنگل تصادفی (RF) با استفاده از رویکرد نقشه‌برداری رقومی خاک (DSM) برای پیش‌بینی اجزا بافت خاک در بخشی از اراضی استان خوزستان انجام شد. در بهمن سال 1399، به‌منظور تعیین بافت خاک، 200 نمونه از خاک سطحی (عمق 10-0 سانتی متری) به صورت تصادفی طبقه‌بندی شده جمع‌آوری شدند. متغیرهای کمکی شامل مشتقات اولیه و ثانویه مدل رقومی ارتفاع (DEM) شامل (شیب، جهت شیب، شاخص شبکه آبراهه‌ای و ...) و شاخص‌های طیفی و گیاهی سنجش از دور (RS) بودند که انتخاب دسته مناسب از آنها با استفاده از روش تجزیه مولفه‌های اصلی (PCA) انجام گرفت. بر اساس روش PCA، نه متغیر توپوگرافی از DEM و هشت شاخص پوشش گیاهی از RS برای پیش‌بینی اجزا بافت خاک (شن، سیلت و رس) انتخاب گردیدند. کارایی مدل‌ها با استفاده از آماره‌های ضریب تبیین (R2) و ریشه‌ی میانگین مربعات خطا (RMSE) مورد ارزیابی قرار گرفت. نتایج نشان داد مدل جنگل تصادفی از دقت بالاتر و خطا کمتری نسبت به مدل ماشین بردار پشتیبان (SVM) برخوردار است، به‌طوری‌که میزان R2 در این مدل برای شن 80/0، سیلت 81/0 و رس 78/0 و ریشه‌ی میانگین مربعات خطا (RMSE) در پیش‌بینی این ذرات به ترتیب 02/6، 89/5 و 02/6 بود. این درحالی است که R2 و RMSE در مدل ماشین بردار پشتیبان به ترتیب برای شن 39/0 و 70/13، سیلت 45/0 و 70/10 و رس46/0 و 32/9 بود. همچنین اهمیت نسبی متغیرهای استفاد شده در پیش‌بینی اجزای بافت خاک نشان داد شاخص شوری، شاخص روشنایی و شبکه آبراهه‌ای به همراه باند 6 ماهواره لندست 8 مهم‌ترین متغیرهای محیطی پیش‌بینی کننده ذرات رس، سیلت و ماسه بودند. بنابراین پیشنهاد می‌شود از مدل جنگل تصادفی به عنوان روشی مفید و قابل اعتماد در تهیه نقشه‌های رقومی بافت خاک در منطقه مورد مطالعه استفاده شود. | ||
| کلیدواژه‌ها | ||
| مدل‌سازی مکانی؛ سنجش از دور؛ ماشین بردار پشتیبان؛ جنگل تصادفی | ||
| عنوان مقاله [English] | ||
| Digital mapping of soil texture components in part of Khuzestan plain lands using machine learning models | ||
| نویسندگان [English] | ||
| Nasim Sahraei1؛ Ahmad Landi2؛ Saeid Hojati3 | ||
| 1Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Khuzestan, Iran | ||
| 2Professor at Department of Soil Science, Faculy of Agriculture, Shahid Chamran University of Ahvaz, Khuzestan, Iran | ||
| 3Associate Professor, Department of Soil Science, College of Agriculture, Shahid Chamran University of Ahvaz | ||
| چکیده [English] | ||
| This study aims to evaluate and to compare the efficiency of support vector machine (SVM) and random forest (RF) models using digital soil mapping approach to predict soil texture in part of Khuzestan province. In February 2021, before determining soil texture, 200 soil samples were taken using stratified random sampling from the surface layer )0-10 cm(. Auxiliary variables included primary and secondary derivatives of digital elevation model (DEM), remote sensing spectral indices (RS), from which the appropriate category was selected using principal component analysis (PCA). Based on PCA method, nine topographic variables from DEM and eight vegetation indices and spectra from RS were selected to predict soils texture components (sand, silt, and clay). The efficiency of the models was evaluated using the coefficient of determination (R2) and the root mean squared of the error (RMSE). The results indicated that the random forest model had higher accuracy and less error than the support vector machine model (SVM), so that values of R2 in this model were 0.80 for sand, 0.81 for silt, and 0.78 for clay, and the RMSE in the prediction of these particles were 6.02, 5.89 and 6.02, respectively. While the R2 and RMSE in the support vector machine model for prediction of sand, silt and clay were (0.39, 13.70), (0.45, 10.70), and (0.46, 9.32), respectively. Also, the results of this evaluation showed that salinity index, brightness index, and channel network in addition of the 6-band Landsat 8 satellite or the far infrared band were the most important environmental variables predicting clay, silt, and sand particles. In conclusion, we suggest using Random Forest model as a useful and reliable method in preparing digital maps of soil texture in the study area. | ||
| کلیدواژه‌ها [English] | ||
| Spatial Modeling, Remote Sensing, Soil Texture, Support Vector Machine, Random Forest | ||
| مراجع | ||
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