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مقایسه روش‌های مختلف داده‌کاوی در پیشبینی ذخیره کربن آلی خاک در برخی اراضی شهرستان بهبهان | ||
| تحقیقات آب و خاک ایران | ||
| مقاله 19، دوره 51، شماره 4، تیر 1399، صفحه 1041-1054 اصل مقاله (1.39 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2020.288526.668308 | ||
| نویسندگان | ||
| صاحب خورده بین* 1؛ سعید حجتی2؛ احمد لندی3؛ ایمان احمدیان فر4 | ||
| 1دانشجوی دکتری، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
| 2دانشیار، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
| 3استاد، گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران | ||
| 4استادیار، گروه عمران، دانشکده فنی و مهندسی، دانشگاه صنعتی خاتم الانبیاء بهبهان، بهبهان، ایران | ||
| چکیده | ||
| ذخیره کربن نقش مهمی در تعیین چرخه جهانی کربن و تنظیم اقلیم جهانی ایفا می‌کند و از طرفی خاک منبع ورودی یا خروجی کربن به اتمسفر است، که به نوع کاربری اراضی وابسته است. از این رو، این مطالعه با هدف مقایسه روش‌های مختلف داده‌کاوی در پیشبینی ذخیره کربن آلی خاک در کاربری‌های کشاورزی آبی، کشت مخلوط (آبی- دیم)، مرتع و نخلستان موجود در بخشی از اراضی شهرستان بهبهان انجام گرفت. نمونه‌برداری از خاک به روش هایپرکیوب انجام و پس از تعیین موقعیت مکانی نقاط نمونه‌برداری، حفر پروفیل و برداشت نمونه‌های خاکی از عمق 30-0 و 60-30 سانتی‌متر انجام گرفت. کربن آلی نمونه‌های خاکی به روش والکی‌بلک و وزن مخصوص ظاهری آنها به روش پارافین مذاب تعیین شد و سپس ذخیره کربن آلی خاک در نقاط نمونه‌برداری محاسبه گردید. پارامترهای کمکی مورد استفاده شامل اجزای سرزمین، داده‌های تصویر سنجنده OLI لندست 8 و نقشه کاربری اراضی بود. نتایج نشان داد که شاخص‌های SAVI، NDVI، شوری، کربنات، گچ و رس بیشترین همبستگی را با مقادیر ذخیره کربن دارند. نتایج همچنین نشان داد که در همه کاربری‌ها، مدل جنگل تصادفی (RF) با بالاترین ضریب تبیین (966/0=R2) و کمترین مجذور میانگین مربعات خطا (032/2=RMSE) بیشترین کارآیی را در پیشبینی ذخیره‌کربن آلی خاک دارد و پس از آن مدل شبکه عصبی مصنوعی (788/0= R2 و 257/4=RMSE) و در نهایت مدل ماشین بردار پشتیبان (SVR) (499/0= R2و 344/7 =RMSE) قرار دارد. | ||
| کلیدواژه‌ها | ||
| واژه‌های کلیدی: بهبهان؛ جنگل تصادفی؛ شبکه عصبی مصنوعی؛ ذخیره کربن؛ ماشین بردار پشتیبان | ||
| عنوان مقاله [English] | ||
| Comparison of Different Data Mining Methods in Predicting Soil Organic Carbon Storage in Some Lands of Behbahan City | ||
| نویسندگان [English] | ||
| saheb khordehbin1؛ Saeid Hojati2؛ Ahmad Landi3؛ iman Ahmadianfar4 | ||
| 1PhD Student, Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 2Associate Professor, Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 3Professor, Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran | ||
| 4Assistant Professor, Department of civil Engineering, Faculty of Engineering, Behbahan Khatam Alanbia University of technology, Behbahan, Iran, | ||
| چکیده [English] | ||
| Abstract: Soil organic carbon is an important factor in determining the global carbon cycle and global climate regulation. Soil is also the input/output source of carbon to the atmosphere which is depended on the land use. For this purpose, the objective of this study was to compare different methods of data mining in predicting soil organic carbon storage in irrigated, mixed cultivation (irrigated and rainfed), pasture and palm trees lands in some parts of Behbahan city in southwestern of Iran. Soil sampling from depths of 0-30 and 30-60 cm was carried out using conditional Latin hypercube square method. Organic carbon content of the soil samples was determined by Walky-Black method. Bulk density of the soils was determined using paraffin method. The auxiliary parameters used in this study included territory components, OLI sensor image data from landsat 8 and land use map. The results showed that the SAVI, NDVI, NDSI, salinity, carbonate, gypsum and clay indices have the highest correlation with the soil organic carbon stock values. The results also showed that the random forest (RF) (R2= 0.983, RMSE=2.32) was the best model to predict soil organic carbon storage followed by artificial neural network model (R2= 0.887, RMSE= 4.257) and Support Vector Regression Machine model (SVR) (R2 = 0.707, RMSE=7.344). | ||
| کلیدواژه‌ها [English] | ||
| Keywords: behbahan, artificial neural network, carbon store, random forest, support vector machine | ||
| مراجع | ||
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