مقاله انگلیسی رایگان در مورد نقشه با قابلیت رانش زمین مبتنی بر داده افریقا – الزویر ۲۰۱۸

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله نقشه با قابلیت رانش زمین مبتنی بر داده افریقا
عنوان انگلیسی مقاله A data-based landslide susceptibility map of Africa
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۸۳ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله مقاله مروری (review article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) scopus – master journals – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF) ۷٫۴۹۱ در سال ۲۰۱۷
شاخص H_index ۱۵۳ در سال ۲۰۱۸
شاخص SJR ۳٫۳۳۴ در سال ۲۰۱۸
رشته های مرتبط زمین شناسی
گرایش های مرتبط زمین شناسی زیست محیطی
نوع ارائه مقاله ژورنال
مجله / کنفرانس بررسی های علوم زمین – Earth-Science Reviews
دانشگاه Department of Earth and Environmental Sciences – Belgium
کلمات کلیدی حرکت توده ای؛ موجودی لغزش؛ گوگل ارث؛ توپوگرافی؛ لرزه خیزی؛ آب و هوا؛ دمای هوا؛ عملکرد رسوب
کلمات کلیدی انگلیسی mass movement; landslide inventory; Google Earth; topography; seismicity; climate; air temperature; sediment yield
شناسه دیجیتال – doi
https://doi.org/10.1016/j.earscirev.2018.05.002
کد محصول E9526
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
۱ Introduction
۲ Materials and methods
۳ Results and discussion
۴ Conclusions
References

بخشی از متن مقاله:
ABSTRACT

Our understanding of the spatial patterns of landslides in Africa is limited with available landslide studies typically focusing on only one or a few study areas. Moreover, Africa is clearly underrepresented in terms of available landslide inventories. This study aims to produce a first continent-wide landslide susceptibility map for Africa, calibrated with a well-distributed landslide dataset. We reviewed the literature on landslides in Africa and compiled all available landslide inventories (ca. 10,800 landslides), supplemented by additional landslide mapping using Google Earth imagery in underrepresented regions (ca. 7,250 landslides). This resulted in a dataset of approximately 18,050 landslides. Various environmental variables were investigated for their significance in explaining the observed spatial patterns of landslides. To account for potential mapping biases in the dataset, we used Monte Carlo simulations that selected different subsets of mapped landslides to test the significance of the considered environmental variables. Based on these analyses, we constructed two landslide susceptibility maps for Africa: one for all landslide types and one excluding the known rockfalls. In both maps, topography is by far the most significant variable. We evaluated the performance of the fitted multiple logistic regression models using independent subsets of landslides, selected from the total dataset. Overall, both maps perform very well in predicting intra-continental patterns of landslides in Africa and explain about 80% of the observed variance in landslide occurrence. To further test the robustness and sensitivity to mapping biases, we also modelled landslide susceptibility while excluding regions with arid climates, as landslides in these environments are expected to be better preserved over time and therefore likely relatively overrepresented. Despite this potential bias, the effect on the landslide susceptibility model is limited. Based on the constructed database and our analyses we further discuss potential research gaps for landslide prediction in Africa and at continental scales. For example, analysis of the African countries’ mean landslide susceptibility shows a lack of landslide research in various countries prone to landsliding (e.g.: Guinea, Gabon, Lesotho, Madagascar). Apart from the intrinsic value of this landslide susceptibility map as a natural hazard risk management tool, the map and compiled database are highly promising for other applications. For example, we explored the potential significance of landslides as a geomorphic process by confronting our landslide susceptibility map with an available database of measured catchment sediment yield for 500 rivers in Africa. Overall, a significant positive, but relatively weak relation between landslide susceptibility and sediment yield is observed.

Introduction

Studies on landslide risks and fatalities indicate that landslides are present on all continents and are a global threat to humans, infrastructure and the environment (Dilley, 2005; Guzzetti et al., 2012; Haque et al., 2016; Kjekstad & Highland, 2009; Petley, 2012; Sassa & Canuti, 2009; Stanley & Kirschbaum, 2017). While this is certainly also the case for Africa, this continent remains strongly underrepresented in landslide research (e.g. Gariano & Guzzetti, 2016; Kirschbaum et al., 2015, 2010; Maes et al., 2017; Nadim et al., 2006; Petley, 2012; Reichenbach et al., 2018). Also global landslide susceptibility (LSS) maps rely on very few (or no) data of observed landslides in Africa for their calibration (e.g. Hong et al., 2007; Kirschbaum et al., 2009; Nadim et al. 2006; Stanley & Kirschbaum, 2017). Nonetheless, landslides are one of the deadliest natural disasters in Africa (Guha-Sapir et al., 2017). Moreover, their importance and impact are expected to increase due to climate change, with an increase in total precipitation and increasing frequency and intensity of rainstorm events (Gariano & Guzzetti, 2016). This was for instance shown for the Great Lakes region (Shongwe et al., 2011; Souverijns et al., 2016; Thiery et al., 2016), probably one of the most landslide susceptible regions in Africa (Hong et al., 2007; Stanley & Kirschbaum 2017). In addition, Africa is facing a considerable growth in population, which is projected to triple by the end of the 21st century (Gerland et al., 2014). This will likely affect both the frequency and impact of landslide events. For these reasons, earlier studies have urged to fill the gap on landslide research in Africa (e.g. Gariano & Guzzetti, 2016; Jacobs et al., 2016; Maes et al., 2017).

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