相关实验视频

Updated: Apr 6, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

使用机器学习来评估波兰未监测水域的生态状况

Andrzej Martyszunis¹, Małgorzata Loga², Karol Przeździecki³

¹Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, Nowowiejska 20 St., 00-653, Warsaw, Poland. andrzej.martyszunis.dokt@pw.edu.pl.

Scientific reports

|October 18, 2024

概括

机器学习 (ML) 模型,包括XGBoost和随机森林,有效地使用人为压力数据评估未被监测的河流的水状态. 这种基于人工智能的方法有助于实现欧洲水框架指令 (WFD) 的环境保护目标.

科学领域:

环境科学环境科学
计算机科学计算机科学
水资源管理水资源管理

背景情况:

欧盟的水框架指令 (WFD) 要求对所有欧洲水体进行水位评估.
密集监测计划是资源密集的,特别是在没有监测的河流.
人工智能 (AI) 为环境管理提供了新的分析工具.

研究的目的:

评估机器学习 (ML) 技术作为评估波兰未监测的河流水体水质状况的补充方法.
通过利用人为压力作为替代数据来应对缺少监测数据的挑战.
确定最有效的ML算法,根据水体水域指令要求对水体状况进行分类.

主要方法:

实现各种ML算法:决策树,随机森林,KNN,支持矢量机,多项天真Bayes和XGBoost.
利用河流流域内的人为压力作为ML模型的输入数据.
使用整体准确度 (OA) 和错误分类概率 (PoM) 进行模型性能比较.

主要成果:

XGBoost和随机森林算法在分类未监测的水体方面表现出最高的效率.
在二进制分类中获得了大约93%的整体准确性 (OA),在综合分类中达到72%.
部分类准确性和错误分类概率 (PoM) 也是关键的比较指标.

结论:

人工智能,特别是ML,为评估未经监测的水体提供了实际解决方案,支持WFD目标.
使用ML的二元分类适用于报告水的状态,而完整分类则有助于规划和运营用途.
整体准确性 (OA) 和错误分类概率 (PoM) 是评估水状况评估分类绩效的有效措施.

关键词:

生态状况评估评估生态状况评估机器学习机器学习没有监控的水体.评估水质水质的评估.

更多相关视频

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

Bioindication Testing of Stream Environment Suitability for Young Freshwater Pearl Mussels Using In Situ Exposure Methods

Bioindication Testing of Stream Environment Suitability for Young Freshwater Pearl Mussels Using In Situ Exposure Methods

Published on: September 5, 2018

相关概念视频

Testing Water Quality

Testing Water Quality

When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...

Freshwater Microbial Ecology

Freshwater Microbial Ecology

Freshwater systems such as streams, rivers, and lakes exhibit distinct physical and biological characteristics that influence their microbial communities. These environments are broadly categorized into lotic systems—those with flowing waters like streams and most rivers—and lentic systems, which include still or slow-moving waters such as lakes, ponds, and marshes.In lentic systems, phytoplankton drive primary production, generating autochthonous organic carbon. In contrast, lotic...