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Updated: Feb 7, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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在Bauchi进行洪水风险预测和建模:利用机器学习模型和可解释的人工智能来实现城市弹性

Kamil Muhammad Kafi1,2, Zakiah Ponrahono1, Zulfa Hanan Ash'aari1

  • 1Department of Environment, Faculty of Forestry and Environment, Universiti Putra Malaysia 43400 Serdang Selangor, Malaysia.

The journal of climate change and health
|February 6, 2026
PubMed
概括

使用人工智能 (AI) 模型准确预测洪水风险,确定了尼日利亚Bauchi的高风险地区. 随机森林 (RF) 模型表现出卓越的表现,突出了定居点的形式性,高度,人口和降雨量作为关键的洪水风险因素.

关键词:
气候变化:极端事件:灾害:非洲:公共卫生:城市规划

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