预测微塑料积聚区和沿着马来西亚凯兰丹海岸的海岸线变化,使用集成的GIS和ANN模型
Muhammad Zakwan Anas Abd Wahid1, Anis Syuhada Saufi1, Nurul Aliah Farhana Kamarulzaman1
1EMZI-UiTM Nanoparticles Colloids & Interface Industrial Research Laboratory (NANO-CORE), Chemical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh, 13500, Pulau Pinang, Malaysia.
Marine pollution bulletin
|July 4, 2025
概括
这项研究使用了结合地理信息系统 (GIS) 和人工神经网络 (ANN) 的新型模型,绘制了马来西亚海岸的微塑料热点. 易受侵蚀的地区被确定为关键的微塑料积聚区,有助于污染控制工作.
科学领域:
- 环境科学 环境科学
- 海洋污染 海洋污染
- 数据建模数据建模
背景情况:
- 微塑料污染是沿海地区日益严重的全球性问题.
- 了解微塑料的空间分布和积累至关重要,但具有挑战性.
- 现有的方法缺乏对微塑料热点的全面预测能力.
研究的目的:
- 开发和验证一个集成的GIS和ANN模型,用于预测微塑料积聚区.
- 确定影响沿着马来西亚凯兰丹海岸微塑料沉积的关键环境驱动因素.
- 为有效的沿海污染管理提供数据驱动工具.
主要方法:
- 综合地理信息系统 (GIS) 和人工神经网络 (ANN) 的建模.
- 环境变量分析:海岸线侵蚀,潮影响和沉积物运输.
- 通过R2 = 0.972.2,实现了高预测准确度.
主要成果:
- 确定易受侵蚀的沿海地区作为显著的微塑料沉积区.
- 证明了潮动力学和微塑料丰富度之间存在强烈的相关性.
- 验证了该模型对微塑料积累的高预测准确性.
结论:
- 集成的GIS-ANN模型对预测沿海环境中的微塑料积累有效.
- 水力动力学力量,特别是潮影响,在微塑料的分布中起着至关重要的作用.
- 这项研究为沿海管理人员提供了一个可扩展的工具,以优化对海洋污染的干预.
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