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使用机器学习算法和孟加拉国气候变化场景的未来地下水潜力绘制地图
Showmitra Kumar Sarkar1, Rhyme Rubayet Rudra2, Swapan Talukdar3
1Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh. mail4dhrubo@gmail.com.
Scientific reports
|May 6, 2024
概括
这项研究使用机器学习和气候变化场景来绘制未来的地下水潜在区域. 人工神经网络 (ANN) 证明是最准确的,识别了具有高和极高地下水潜力的重要区域.
科学领域:
- 水文学 水文学
- 环境科学 环境科学
- 地理空间分析是什么
背景情况:
- 准确地绘制地下水潜在区域地图对于可持续的水资源管理至关重要.
- 气候变化对水文系统产生重大影响,需要面向未来的评估.
- 机器学习为整合复杂的环境数据提供了强大的工具,用于预测建模.
研究的目的:
- 用机器学习算法和各种气候变化场景来估计未来的地下水潜在区域.
- 为了比较人工神经网络 (ANN),物流模型树 (LMT) 和物流回归 (LR) 对地下水潜力绘图的性能.
- 在不同的代表性度路径 (RCP) 下对近未来几年进行地下水潜在区域的预测.
主要方法:
- 利用十四个地理空间和气候参数 (例如斜率,降雨量,地质,土地使用) 作为机器学习模型的输入.
- 应用并比较ANN,LMT和LR模型来划定地下水潜在区域,通过ROC曲线分析选择最佳模型.
- 从RCP场景 (2.6,4.5,6.0,8.5) 中整合未来降水数据,预测2025年,2030年,2035年和2040年的地下水潜在区域.
主要成果:
- 人工神经网络 (ANN) 与LMT和LR相比显示出更高的精度 (AUC:0.875).
- 该模型确定了具有非常高 (23.10%) 和极高 (33.50%) 地下水潜力的重要区域.
- 在各种RCP场景和时间点生成了16个不同的未来地下水潜在区域地图.
结论:
- 机器学习,特别是ANN,对于预测气候变化下的未来地下水潜在区域非常有效.
- 这些发现为国家水资源规划和基于证据的决策提供了关键的空间信息.
- 积极的水资源管理策略对于应对地下水可用性的预计变化至关重要.
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