用深度学习融合多种海洋数据，预测印度洋渔场集中区。
Predicting Weekly Fishing Concentration Zones through Deep Learning Integration of Heterogeneous Environmental Spatial Datasets
- 融合海表温度、叶绿素浓度等多源海洋数据，构建渔场预测模型。
- 初步结果表明可减少寻鱼时间、降低燃油消耗，提升资源利用效率。
- 适合渔业管理、智能导航与可持续捕捞研究者参考。
北印度洋（包括阿拉伯海和孟加拉湾）是沿海社区的重要生计来源，但渔民常难以确定高产渔场位置。为应对这一挑战，我们提出一种基于人工智能的框架，通过海表温度、叶绿素浓度等海洋学参数预测潜在渔场区域（PFZ），旨在提高渔场识别准确率，并提供区域性洞察以支持可持续渔业实践。初步结果显示，该框架有助于减少渔民搜寻时间、降低燃油消耗，并促进资源高效利用。
原文摘要 · Abstract (English)
The North Indian Ocean, including the Arabian Sea and the Bay of Bengal, represents a vital source of livelihood for coastal communities, yet fishermen often face uncertainty in locating productive fishing grounds. To address this challenge, we present an AI-assisted framework for predicting Potential Fishing Zones (PFZs) using oceanographic parameters such as sea surface temperature and chlorophyll concentration. The approach is designed to enhance the accuracy of PFZ identification and provide region-specific insights for sustainable fishing practices. Preliminary results indicate that the framework can support fishermen by reducing search time, lowering fuel consumption, and promoting efficient resource utilization.
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