Determine of Correlation Coefficient between EPM and MPSIAC Models and Generation of Erosion Maps by GIS Techniques in Baghmalek Watershed, Khuzestan, Iran
سال انتشار: 1390
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,634
فایل این مقاله در 12 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
SASTECH05_058
تاریخ نمایه سازی: 22 مرداد 1391
چکیده مقاله:
Soil erosion is a major threat to global economic and environmental sustainability. Soil erosion is one of the huge propellant and recognized economical, social and environmental factors and has been performed great effort in most country for its struggle and inspection and has been spent exorbitant sums. In this study with the aim of inspection of efficiency of two Erosion Potential methods (EPM) and Modify Pacific Southwest Interagency Committee (MPSIAC) models, we used to estimating of erosion and sediment in Abdullah Baghmalek watershed. The area is about 105 Km2 with annual precipitation about 700-800 mm, sensitive with annual precipitation about 700-800 mm, sensitive related calculations to each model were performed. PSIAC model has been chosen as a target due to lack of sediment testing station. Statistical comparison has been preformed too with SPSS. Results show that MPSIAC together with GIS&RS techniques more solidarity with sediment rate and EPM model just does a brief consideration from erosion and sediment measure in area. According to the results, in MPSIAC and PSIAC, parcel No.1 showed minimum and parcel No.5 maximum sediment yields. While in EPM parcel No.1 showed minimum and parcel No.6 maximum sediment yield rate in area level. Regression lines showed that MPSIAC sediment graph has the highest correlation with control model. R2 for PSIAC is 0.6068, R2 for EPM is 0.4988 and R2 for MPSIAC is 0.5967
نویسندگان
Y Ghobadi
SNML Dept., Institute of Advanced Technology (ITMA), University Putra Malaysia
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :