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Computational Approaches to Big Data...

Barnes, Richard.

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Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology./
作者:	Barnes, Richard.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	165 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28090506
ISBN:	9798691237423

Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology.
Barnes, Richard.

Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 165 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2020.

This item must not be sold to any third party vendors.

Some of the most challenging problems in ecology, hydrology, and geomorphology arise from processes which play out over large spatial scales and long time intervals. High-resolution global digital elevation models are becoming available. They will allow problems of broad spatial extent to be addressed, but only if we can handle the enormous volume of data. Similarly, performance gains in computers now make it feasible to test more complex theory using computational models, but only if efficient techniques are used. In this dissertation I develop and demonstrate techniques for handling large geospatial raster datasets and rapidly modeling ecological and hydrological processes, producing results that are orders of magnitude more efficient than previous work. Notably, these techniques work on both high-performance machines and laptops. Finally, I apply the techniques to a challenging problem at the interface of ecology, evolution, climate, and geology.

ISBN: 9798691237423Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computational science

Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology.
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