نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی کارشناسی ارشد، رشتۀ مدیریت و کنترل بیابان، گروه احیای مناطق خشک و کوهستانی، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران.
2 دانشیار، گروه احیای مناطق خشک و کوهستانی، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران.
3 استادیار، گروه احیای مناطق خشک و کوهستانی، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران.
4 استاد، گروه احیای مناطق خشک و کوهستانی، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران.
5 استادیار، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان کردستان، سنندج، ایران.
چکیده
مدلسازی یکی از ابزارهای مناسب برای تصمیم‌گیری پدیده‌های محیط‌زیستی می‌باشد که به صورت مدلهای مفهومی یا روابط ریاضی بیان می‌شوند. هدف از این تحقیق مقایسۀ مدلهای یادگیری ماشینی شامل ماشین بردار پشتیبان، درخت طبقهبندی و رگرسیون، جنگل تصادفی و مدل آنالیز تشخیص ترکیبی جهت اولویتبندی مناطق مستعد گرد و غبار است. جهت تعیین روزهای گرد و غبار از دادههای ساعتی هواشناسی استانهای البرز و قزوین و تصاویر ماهوارهای مربوط به همان روزها برای دورۀ 2000 تا 2019 استفاده شد. 420 نقطۀ برداشت گرد و غبار در منطقه شناسایی و نقشۀ پراکنش آنها تهیه گردید. سپس نقشههای عوامل تأثیرگذار بر وقوع گرد و غبار شامل نقشههای کاربری اراضی، خاکشناسی، شیب، جهت، ارتفاع، پوشش گیاهی، رطوبت سطح توپوگرافیکی، نسبت سطح توپوگرافیکی و زمین‌شناسی تهیه گردید. با استفاده ازمدلهای ذکر شده تأثیر هر یک از عوامل مؤثر گرد و غبار مشخص و نقشههای اولویتبندی مناطق برداشت گرد و غبار تهیه شد. ارزیابی مدلها با استفاده از منحنی راک صورت گرفت. طبق نتایج حاصل شده عامل ارتفاع در تمامی مدلها نسبت به سایر پارامترهای مورد استفاده در مدل از اهمیت بیشتری برخوردار است. نتایج مدلسازی نیز نشان داد مدلهای جنگل تصادفی (RF) و مدل آنالیز تشخیص ترکیبی MDA)) دارای بیشترین مقادیر صحت (96/0)، دقت (94/0)، احتمال آشکارسازی (98/0) و کمترین نرخ هشدار اشتباه (051/0) نسبت به بقیۀ مدلها است. عملکرد مدلهای RF و MDA نسبت به سایر مدلها بهتر بوده و پس از آنها به ترتیب مدلهای ماشین بردار پشتیبان ((SVM و درخت طبقهبندی و رگرسیون CART)) قرار دارند. همچنین در ارزیابی مدلها با استفاده از منحنی مشخصۀ عملکرد (ROC)، مدل RF به عنوان بهترین مدل انتخاب شد.
کلیدواژه‌ها
عنوان مقاله [English]
Comparison of machine learning models to prioritize susceptible areas to dust production
نویسندگان [English]
- Serveh Darvand 1
- Hassan Khosravi 2
- Hamidreza Keshtkar 3
- Gholamreza Zehtabian 4
- Omid Rahmati 5
1 M.Sc. Student, Desert Management and Control, Faculty of Natural Resources, University of Tehran, Iran
2 Associate Professor, Faculty of Natural Resources, University of Tehran
3 Assistant Professor, Faculty of Natural Resources, University of Tehran, Iran
4 Professor, Faculty of Natural Resources, University of Tehran, Iran
5 Assistant Professor, Agricultural Research Education and Extension Organization, Sanandaj, Iran
چکیده [English]
The purpose of this study was to compare machine learning models including Support Vector Machine, Classification and Regression Tree, Random Forest, and Multivariate Discriminate Analysis to prioritize susceptible areas to dust production. To determine the dust days, hourly meteorological data of Alborz and Qazvin provinces and satellite images of the same days for the period 2000 to 2019 were used. 420 dust collection points were identified and the map of their distribution was prepared. The maps of factors affecting the occurrence of dust, including landuse map, soil orders map, slope map, slope aspect map, elevation map, vegetation map, topographic surface moisture, topographic surface ratio, and geology mam were prepared. Using the mentioned models, the impact of each of the effective factors of dust was determined and prioritization maps of dust harvesting areas were prepared. Models were evaluated using the ROC curve. According to the results, the elevation factor is more important in all models than the other parameters used in the model. The modeling results also showed that the Random Forest )RF( and Multivariate Discriminate Analysis (MDA) models had the highest values of accuracy (0.96), precision (0.94), Probability of Detection (POD) (0.98), and False Alarm Ratio (FAR) (0.051) compared to the others. The performance of the RF and MDA models is better than the other models, followed by the Support Vector Machine (SVM) and Classification and Regression Tree (CART) models, respectively. Also, in evaluating the models using Receiver Operating Characteristic (ROC), the RF model was selected as the best model.
کلیدواژه‌ها [English]
- Alborz
- Qazvin
- Satellite images
- Dust
- Machine learning
- ROC curve
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