利用双变量统计技术在印度查谟和克什米尔喜马拉雅山西北部划分区域滑坡易发带

IF 1.3 4区地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Journal of Earth System Science Pub Date : 2024-08-10 DOI:10.1007/s12040-024-02367-3

Imran Khan, Harish Bahuguna, Ashutosh Kainthola

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Regional landslide susceptibility zonation utilizing bivariate statistical techniques in the northwestern Himalayas, Jammu and Kashmir, India

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Regional landslide susceptibility zonation utilizing bivariate statistical techniques in the northwestern Himalayas, Jammu and Kashmir, India

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来源期刊

Journal of Earth System Science Earth and Planetary Sciences-General Earth and Planetary Sciences

CiteScore

3.20

自引率

5.30%

发文量

226

期刊介绍： The Journal of Earth System Science, an International Journal, was earlier a part of the Proceedings of the Indian Academy of Sciences – Section A begun in 1934, and later split in 1978 into theme journals. This journal was published as Proceedings – Earth and Planetary Sciences since 1978, and in 2005 was renamed ‘Journal of Earth System Science’. The journal is highly inter-disciplinary and publishes scholarly research – new data, ideas, and conceptual advances – in Earth System Science. The focus is on the evolution of the Earth as a system: manuscripts describing changes of anthropogenic origin in a limited region are not considered unless they go beyond describing the changes to include an analysis of earth-system processes. The journal''s scope includes the solid earth (geosphere), the atmosphere, the hydrosphere (including cryosphere), and the biosphere; it also addresses related aspects of planetary and space sciences. Contributions pertaining to the Indian sub- continent and the surrounding Indian-Ocean region are particularly welcome. Given that a large number of manuscripts report either observations or model results for a limited domain, manuscripts intended for publication in JESS are expected to fulfill at least one of the following three criteria. The data should be of relevance and should be of statistically significant size and from a region from where such data are sparse. If the data are from a well-sampled region, the data size should be considerable and advance our knowledge of the region. A model study is carried out to explain observations reported either in the same manuscript or in the literature. The analysis, whether of data or with models, is novel and the inferences advance the current knowledge.