在埃塞俄比亚的Genale Dawa河流盆地整合遥感和机器学习算法,用于农业干旱早期预警,埃塞俄比亚
Mikhael G Alemu1,2, Fasikaw A Zimale3
1Department of Climate Change Engineering, Pan African University Institute for Water and Energy Sciences -Including Climate Change (PAUWES), Tlemcen, Algeria. michaelgetu22@gmail.com.
Environmental monitoring and assessment
|February 4, 2025
概括
农业干旱在埃塞俄比亚构成了重大威胁.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 非洲之角,特别是埃塞俄比亚的Genale Dawa河流盆地,面临严重的农业干旱.
- 对干旱的脆弱性影响该地区的粮食安全和生计.
研究的目的:
- 在Genale Dawa河流盆地开发农业干旱的预警系统.
- 通过遥感和机器学习技术评估和预测干旱的严重程度.
主要方法:
- 利用高分辨率卫星图像来得出植被状况指数 (VCI),热状况指数 (TCI) 和植被健康指数 (VHI) 从2003-2023.
- 雇佣人工神经网络 (ANN) 机器学习来预测2028年和2033年的VHI.
- 分析了盆地内不同地区的干旱严重程度.
主要成果:
- 在2023年,低盆地地区 (Dolo ado,Chereti) 发生了25%的严重和18%的极端干旱.
- 极端干旱与高TCI (23.24%) 和低降水 (<3.57毫米/月) 在莫亚莱,多洛阿多,多洛巴伊,阿夫德和布雷相关.
- 预计到2028年和2033年在特定盆地地区的严重和极端干旱VHI值的增加.
结论:
- 该研究为Genale Dawa河流域的干旱管理和适应策略提供了关键数据.
- 调查结果支持及时做出决定,为农业恢复力和干旱应对机制提供支持.
- 强调综合遥感和机器学习对于干旱预警系统的有用性.
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