基于SSAPSO优化预测追求模型的黑龙江回收区农业干旱灾害风险评估研究
1College of Arts and Sciences, Northeast Agricultural University, Changjiang Street No. 600, Harbin, 150030, People's Republic of China.
Environmental monitoring and assessment
|April 25, 2024
概括
中国黑龙江回收区的农业干旱风险随着时间的推移而下降,这是由于抗性能力的提高. 然而,空间差异仍然存在,尽管经济增长,但西部地区面临更高的风险.
科学领域:
- 农业科学 农业科学
- 环境风险评估环境风险评估
- 食品安全研究 食品安全研究
背景情况:
- 黑龙江回收区对于中国的粮食生产和粮食安全至关重要.
- 了解农业干旱风险对于减轻灾害和尽量减少粮食损失至关重要.
- 干旱风险的区域差异需要定制的预防和减缓战略.
研究的目的:
- 评估黑龙江回收区的农业干旱风险.
- 分析干旱风险的空间和时间变化.
- 确定影响干旱风险的因素,并为缓解风险的策略提供信息.
主要方法:
- 一个包含17个指标的综合指标系统 (危险,暴露,脆弱性,抵抗能力).
- 区域干旱风险评估的预测追踪模型.
- 基于Sparrow算法改进的粒子群集优化 (SSAPSO) 用于算法比较和选择.
主要成果:
- 随着时间的推移,农业干旱风险普遍下降,暴露率增加,抗旱能力提高.
- 存在显著的空间差异,西方地区由于危险,脆弱性和较低的抗性而存在更高的风险.
- 经济增长和农业专业知识与降低干旱风险相关,但区域经济差异造成了不同的风险分布.
结论:
- SSAPSO算法在干旱风险评估方面表现出卓越的优化性能.
- 尽管总体降低了风险,但空间异质性需要有针对性的干预措施.
- 可持续的农业发展和区域经济对齐是管理未来干旱风险的关键.
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