深度学习模型与软计算技术的创新综合方法,用于在西孟加拉邦Rarh地区基于GIS的干旱区域测绘
Gopal Chowdhury1, Sayantan Mandal2, Ashis Kumar Saha2
1Department of Geography, Delhi School of Economics, University of Delhi, Delhi, India. gopalchowdhury989@gmail.com.
概括
这项研究使用混合深度学习合奏模型绘制了西孟加拉邦干旱易感地区的地图. 它发现了严重的干旱风险,特别是在Purba Bardhhaman,突出了针对干旱管理战略的需要.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 干旱对生态系统和社会构成重大风险,需要准确识别才能有效缓解.
- 西孟加拉邦的Rarh地区,特别是Birbhum和Purba Bardhhaman地区,面临降雨趋势下降和严重的干旱状况.
- 农业和粮食安全受到干旱的威胁,特别是在像Purba Bardhhaman这样的生产大米的地区.
研究的目的:
- 分析西孟加拉邦Rarh地区的干旱情景,每隔3个月,6个月和12个月.
- 使用混合深度学习合奏模型开发精确的干旱易感地图.
- 为决策者提供可操作的见解,以管理干旱风险并确保可持续发展.
主要方法:
- 分析了27个气象,农业,水文和社会经济领域的干旱评估因素.
- 使用多层感知神经网络 (MLP NN) 作为基准.
- 开发并应用了混合深度学习合奏模型用于干旱映射.
主要成果:
- 该研究确定了明显的干旱易感地区:26.66%在3个月,20%在6个月和25%在12个月.
- 混合深度学习组合模型表现出高精度,实现ROC-AUC值为94.2% (3个月),94.3% (6个月) 和95.3% (12个月).
- 干旱严重影响了普尔巴巴德哈曼的产量和粮食安全.
结论:
- 混合深度学习组合模型提供了一个精确的干旱风险评估方法.
- 调查结果强调,迫切需要在西孟加拉州制定量身定制的干旱管理战略.
- 了解空间干旱的范围和原因对于有效的减缓和可持续发展至关重要.
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