分析斯里兰卡渔业受气候与突发事件影响，提出多维度预测框架。
Monsoon Mayhem to Market Waves: Forecasting Fisheries Resilience in Sri Lanka
- 结合气候、事件与生产价格数据，构建跨领域预测模型。
- 2019–2025年数据显示沿海与内陆渔业响应差异显著，部分可相互补偿。
- 适合政策制定者、供应链管理者用于风险预警与韧性规划。
斯里兰卡渔业对就业与粮食供应至关重要。2019至2025年间，该行业面临多重并发重大挑战，但这些事件如何共同影响鱼类产量与价格仍不清晰。本研究构建一个整合天气变化、重大扰动事件、鱼类产量与价格的分析框架，旨在支持政策制定者、贸易商与供应链管理者决策。采用STL分解分析季节模式，通过Spearman滞后相关识别气候对产量的延迟影响，使用中断时间序列（ITS）回归评估重大事件冲击，利用SARIMAX模型预测月度产量与价格，并通过热点检测识别异常模式。结果表明，海洋与内陆渔业在季节性及气候响应上存在差异，重大扰动的影响程度不同，且某些情况下一部门可部分弥补另一部门损失。研究建议加强高风险区基础设施、提升冷链系统能力，并以价格预测工具作为决策辅助而非直接市场信号。
原文摘要 · Abstract (English)
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.
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