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多机器学习方法来预测流域规模的总的空间变化特征:来自亚洲最大的流域 (长江流域) 的证据
Xihua Wang1, Xuming Ji2, Y Jun Xu3
1College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China; Department of Earth and Environmental Sciences, University of Waterloo, ON N2L 3G1, Canada.
The Science of the total environment
|August 2, 2024
概括
污染威胁着河流. 农业活动和降雨导致总 (TN) 的变化,上游地区的变化更大. 机器学习模型有助于预测和管理TN风险.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 生态生态学 生态生态学
背景情况:
- 污染对全球河流健康构成重大威胁.
- 在流域尺度上了解和预测总 (TN) 仍然具有挑战性.
研究的目的:
- 调查影响长江流域丰富的因素.
- 开发机器学习模型,用于预测TN度和识别高风险区域.
主要方法:
- 利用了来自530个监测部分的数据.
- 计算了土地利用综合指数,并进行了统计分析.
- 开发并比较TN预测的Random Forest,BP和LSTM机器学习模型.
主要成果:
- 农业活动和降雨是每月TN变化的主要驱动因素.
- 上游地区的TN度变化较高 (0.09711.099毫克/升),而不是中间/下游地区 (0.3486.844毫克/升).
- 随机森林模型实现了77.6%的预测准确度;确定了37个高风险区域.
结论:
- 微生物分解和土地利用变化有助于区域缩差异.
- 城市和工业投入推动了上游TN,而农业主导了中游/下游.
- 机器学习模型为流域缩风险评估提供了可靠的方法.
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