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ارزیابی پتانسیل ریزش ترانشه جاده های جنگلی (مطالعه موردی: حوضه آبخیز 46، شمال ایران) | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 17، دوره 23، شماره 3، مهر 1394، صفحه 573-582 اصل مقاله (276.86 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2015.105661 | ||
| نویسندگان | ||
| زهرا عزیزی* 1؛ اصغر حسینی2 | ||
| 1استادیار، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد | ||
| 2دانشجوی کارشناسی ارشد جنگلداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد | ||
| چکیده | ||
| شبکه جاده‌ها اساس تولید مدرن و پایدار جنگل‌های هیرکانی هستند. این جنگل‌ها اغلب در مناطق پرشیب و کوهستانی واقع شده‌اند، بنابراین پس از ساخت جاده، همواره احتمال وقوع ریزش ترانشه‌های جاده وجود دارد. ارزیابی میزان این احتمال در زمان طراحی و ساخت جاده‌ها از اهمیت بسزایی برخوردار است. در پژوهش پیش‌رو با استفاده از مدل شبکه عصبی مصنوعی ریزش ترانشه‌های جاده مورد ارزیابی قرار گرفت و احتمال وقوع ریزش ترانشه در چهار طبقه بدون ریزش، ریزش کم، ریزش متوسط و ریزش زیاد دسته‌بندی شد. نتایج نشان داد که ریزش کم با 39/8 درصد بیشترین احتمال وقوع و ریزش زیاد (31/3 درصد) کمترین احتمال وقوع را داشته‌اند. این مدل در پیش‌بینی احتمال وقوع ریزش متوسط و عدم وقوع ریزش به‌ترتیب کمترین (78/6 درصد) و بیشترین صحت (92/6 درصد) داشت. بررسی عامل‌های مؤثر در احتمال وقوع ریزش ترانشه‌ها نشان داد که به‌ترتیب زاویه ترانشه نسبت به سطح افق، شیب طبیعی عرصه و ارتفاع ترانشه بیشترین تأثیر و جنس خاک و تعداد درخت تا شعاع 10 متری محل اندازه‌گیری ریزش، کمترین تأثیر را در وقوع ریزش داشته‌اند. | ||
| کلیدواژه‌ها | ||
| جاده جنگلی؛ ریزش ترانشه؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Evaluation of slope failure potential in forest roads (Case study: 46th watershed, North of Iran) | ||
| نویسندگان [English] | ||
| Zahra Azizi1؛ Asghar Hosseini2 | ||
| 1Assistant Prof., Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran | ||
| 2M.Sc. Forestry, Faculty of Natural Resources and Earth Science, Shahrekord University, Shahrekord, Iran | ||
| چکیده [English] | ||
| Forest road networks are the essential bases of modern and sustainable production within Hyrcanian forests of Iran. In most mountainous areas, the slope failures are often caused by the construction of forest road on steep terrains. Therefore, it is crucial to locate forest roads on stable slopes. It should be additionally noted that high-structured forest roads constructed on steep terrain often end up with failure during cut and fill operations. In this study, a model was developed using the Artificial Neural Network to evaluate potential failure of cut and fill in forest. Probability of slope failure was classified in four categories, including no, low, medium and high slope failures. The overall result showed that low slope failure category (with a probability of 39.8%) has high occurrences, whereas high slope failure category (with probability of 13.3%) was associated with fewer occurrences. As modelled by ANN, lowest and highest accuracies for classified slope failure were 92.6% (in no slope failure category) and 78.6% (in medium slope failure category) respectively. Based on importance indices, gradient of trench, natural slope of area and the height of the trench were associated with the greatest influences on the slope failure, while the soil type and number of trees turned out to be of least effect on the slope failure occurrence. | ||
| کلیدواژه‌ها [English] | ||
| forest road, slope failure, Artificial Neural Network | ||
| مراجع | ||
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