季风混乱到市场波动：斯里兰卡渔业韧性预测
Monsoon Mayhem to Market Waves: Forecasting Fisheries Resilience in Sri Lanka
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中文总结 AI 辅助
本研究开发框架结合STL分解、ITS回归等方法，分析斯里兰卡渔业受气候与重大事件的影响，发现海洋与内陆渔业的差异，成果可支撑渔业规划与决策。
中文摘要 AI 辅助
斯里兰卡的渔业部门对就业和食品供应至关重要。2019年至2025年间，该行业同时面临多个重大问题，这些事件共同对鱼类生产和价格产生的影响仍未得到充分理解。本研究开发了一个框架，将天气变化、重大中断事件、鱼类生产与价格关联起来，目标是帮助政策制定者、贸易商和供应链管理者做出更优决策。研究使用STL分解分析季节性模式，采用斯皮尔曼滞后相关性确定气候对生产的延迟效应，通过中断时间序列（ITS）回归衡量重大事件的影响，利用SARIMAX模型预测月度生产与价格，还运用热点检测识别异常模式。结果显示，海洋渔业与内陆渔业在季节性和气候影响方面表现不同；重大中断事件的影响程度各异，部分情况下一个渔业部门可对另一个部门形成补偿。这些发现可支持更优规划，例如改善高风险地区的基础设施、强化冷藏系统、对异常事件使用预警警报；价格预测工具应作为决策支持工具，而非直接市场信号。
英文摘要
Sri Lanka's fisheries sector is important for jobs and food supply. Between 2019 and 2025, it faced several major problems at the same time, and how these events together affected fish production and prices is still not well understood. This study develops a framework to connect weather changes, major disruption events, fish production, and prices, with the goal of helping policymakers, traders, and supply chain managers make better decisions. Seasonal patterns are studied using STL decomposition. Spearman lag correlation is used to find delayed effects of climate on production. Interrupted Time Series (ITS) regression measures the impact of major events. SARIMAX models predict monthly production and prices. Hotspot detection identifies unusual patterns. The results show that marine and inland fisheries behave differently in terms of seasons and climate effects. Major disruptions caused different levels of impact, and in some cases, one sector helped compensate for another. These findings can support better planning, for example, improving infrastructure in high-risk areas, strengthening cold storage systems, and using early warning alerts for unusual events. Price forecasting tools should be used as decision-support tools, not as direct market signals.
发表机构
- University of Moratuwa（莫拉图瓦大学）
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