基于深度神经网络,遥感和GIS的热电厂污染评估:伊朗的一个真实案例研究
Marzieh Mokarram1, Mohammad Jafar Mokarram2, Arsalan Najafi3
1Department of Geography, Faculty of Economics, Management and Social Science, Shiraz University, Shiraz, Iran.
Marine pollution bulletin
|June 1, 2023
概括
班达尔阿巴斯热电厂对波斯湾水域产生重大影响,增加了海面温度 (SST) 并与生态指数相关联. 先进的AI模型准确地预测了这些温度变化.
科学领域:
- 环境科学 环境科学
- 海洋生物学 海洋生物学
- 大气化学 大气化学
背景情况:
- 热电厂可以改变海洋和大气条件.
- 了解这些影响对于沿海生态系统管理至关重要.
研究的目的:
- 评估波斯湾班达尔阿巴斯热电厂对环境的影响.
- 为了将热污染与水质和空气污染指数相关联.
- 使用先进的建模技术预测海面温度 (SST).
主要方法:
- 结合卫星图像和地面数据来测量SST,总有机碳 (TOC),化学氧气需求 (COD) 和大气污染物 (SO2,O3,NO2,CO2,CO,CH4).
- 使用长期短期记忆 (LSTM),支持向量回归 (SVR) 和级联神经网络进行温度预测.
- 使用多重线性回归 (MLR) 来分析变量之间的相关性.
主要成果:
- 电厂附近的SST峰值在7月和9月达到大约50°C.
- 在水中SST和生态指数 (TOC,COD) 之间观察到强烈的相关性 (TOC的R2=0.98).
- 在空气污染物 (O3,CO2,CO) 和温度之间发现了显著的相关性.
结论:
- 班达尔阿巴斯热电厂明显影响了当地海洋和大气环境.
- 在预测水温变化方面,LSTM模型的准确性很高 (R2=0.98).
- 调查结果强调了对热电厂排放的监测和减缓战略的需要.
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