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دسته بندی:
یادگیری عمیق - deep learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Projecting Australias forest cover dynamics and exploring influential factors using deep learning
ترجمه فارسی عنوان مقاله:
پیش بینی پویایی پوشش جنگلی در استرالیا و کشف عوامل مؤثر با استفاده از یادگیری عمیق
منبع:
Sciencedirect - Elsevier - Environmental Modelling and Software, 119 (2019) 407-417: doi:10:1016/j:envsoft:2019:07:013
نویسنده:
Long Yea,b,c, Lei Gaob,c,*, Raymundo Marcos-Martinezd,e, Dirk Mallantsc, Brett A. Bryanf
چکیده انگلیسی:
This study presents the first application of deep learning techniques in capturing long-term, time-continuous
forest cover dynamics at a continental scale. We developed a spatially-explicit ensemble model for projecting
Australias forest cover change using Long Short-Term Memory (LSTM) deep learning neural networks applied to
a multi-dimensional, high-resolution spatiotemporal dataset and run on a high-performance computing cluster.
We further quantified the influence of explanatory variables on the spatiotemporal dynamics of continental
forest cover. Deep learning greatly outperformed a state-of-the-art spatial-econometric model at continental,
state, and grid-cell scales. For example, at the continental scale, compared to the spatial-econometric model, the
deep learning model improved projection performance by 44% (root-mean-square error) and 12% (pseudo Rsquared).
The results illustrate the robustness and effectiveness of the LSTM model. This work provides a reliable
tool for projecting forest cover and agricultural production under given future scenarios, supporting decisionmaking
in sustainable land development, management, and conservation.
Keywords: Long short-term memory | Deep learning | Forest cover change | Spatiotemporal data | Projections | Deforestation
قیمت: رایگان
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