人工神经网络用于孟加拉国洪水易感性测绘
Rhyme Rubayet Rudra1, Showmitra Kumar Sarkar1
1Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh.
Heliyon
|May 30, 2023
概括
这项研究使用人工神经网络绘制了孟加拉国洪水易感性的地图. 调查结果显示,超过78%的Sylhet区面临非常高至高的洪水风险,为关键的缓解策略提供了信息.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 灾害管理 灾害管理
背景情况:
- 洪水对孟加拉国西尔赫特地区构成重大威胁,影响了人们的生活和生计.
- 有效的洪水风险管理需要准确的易感性映射.
研究的目的:
- 调查和绘制孟加拉国西尔赫特地区洪水易感性的地图.
- 确定洪水易感性非常高,高,中,低的地区.
- 为洪水风险缓解策略提供数据驱动的见解.
主要方法:
- 利用了八个有影响力的因素:高度,斜率,角度,曲率,地形湿度指数 (TWI),流动力指数 (SPI),粗度和土地使用/土地覆盖 (LULC).
- 采用人工神经网络 (ANN) 模型进行洪水易感性评估.
- 应用ArcGIS用于空间映射和结果的可视化.
- 使用1280个样本 (75%的培训,25%的测试) 验证了该模型,实现了89%的预测率和98%的成功率.
主要成果:
- 大约40.98%的研究区域属于非常高的洪水易感区 (499,433.50公).
- 另外37.43%的地区被归类为高度易受洪水影响的地区 (456,168.76公).
- 低和中度易感区分别仅占该地区的6.52%和15%.
结论:
- 锡尔赫特分区展示了非常高和高洪水易感性的广泛地区.
- 开发的洪水易感性模型显示出高准确性和可靠性.
- 这些发现对于政策制定者和当局实施有效的洪水风险管理和缓解措施至关重要.
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