Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models

IF 2.3 3区环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES

Journal of Mountain Science Pub Date : 2023-10-01 DOI:10.1007/s11629-023-8158-7

Kuan Shui, Ke-peng Hou, Wen-wen Hou, Jun-long Sun, Hua-fen Sun

引用次数: 0

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基于多层机器学习回归模型的麻雀搜索算法优化边坡安全系数预测

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Mountain Science 环境科学-环境科学

CiteScore

4.20

自引率

8.00%

发文量

182

审稿时长

4.3 months

期刊介绍： The Journal of Mountain Science (JMS) is devoted to mountains and their surrounding lowlands - ecoregions of particular global importance, with a particular emphasis on the important highlands/ mountains in the world, such as the Tibetan Plateau, the Himalayas, the Alps, the Andes, the Rockies and many other mountain ranges of our planet. JMS mainly publishes academic and technical papers concerning environmental changes and sustainable development in mountain areas under natural conditions or / and with the influence of human activities. It also accepts book reviews and reports on mountain research and introductions to mountain research organizations. This journal pays particular attention to the relationships between mountain environment changes and human activities, including the processes, characteristics and restoration of mountain ecosystem degradation; dynamic processes, and the theory and methods of controlling mountain hazards, such as debris, landslides and soil erosion; the protection and development of special mountain resources; culture diversity and local economic development in mountain regions; and ethnic issues and social welfare in mountain areas. Academic papers should display universal, strategic and innovative characteristics in both theory and practice. Technical papers should report on development programmes, project planning and community actions. We especially welcome papers which emphasize the application of new technologies, such as GIS and remote sensing, in mountain research and development, and the papers on new concepts and new methods deriving from disciplinary, interdisciplinary and transdisciplinary research in mountains.