首页|湖库水体藻类浓度预测模型的原理和应用

湖库水体藻类浓度预测模型的原理和应用

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在全球气候变化和人为污染的背景下，湖库型水源地因富营养化暴发水华的风险显著提高，严重影响城市供水安全。因此，有必要构建基于藻类生长影响因子的数学模型，以实现藻类浓度预测和水华预警。本文从物理、化学和生物三个层面归纳了影响藻类生长的主要因子，并在此基础上总结概述了现有预测模型的构建思路和应用场景。根据建模方法可将预测模型分为过程机理模型和数据驱动模型两类。两种建模方法都已有广泛的研究，也在部分湖泊水库实现了应用。前者基于自然过程的研究和解析，具有可解释性和一般性，但有一定的研究和测试门槛且成本较高。后者基于机器学习等人工智能方法，建模方法灵活多样，但依赖数据质量，缺乏机理支持且具有地点特异性。为进一步提高模型性能，今后的研究工作需要提高数据监测的频率和质量，同时将过程机理与人工智能方法相结合。
Principle and Application of Algae Concentration Prediction Models in Lakes and Reservoirs
The risk of algal blooms has significantly increased in eutrophic lakes and reservoirs due to the global climate change and anthropogenic pollution,which has a significant impact on the safety and stability of municipal water supplies.To protect source water,it is necessary to construct a mathematical model and alert system to predict algae concentration in lakes and reservoirs.This paper reviews the main environmental factors(physical,chemical,and biological)that affect the algae growth,and summarizes the principles and application scenarios of existing models.Prediction models can generally be divided into two categories:process-based models(PB models)and data-driven models(DD models).PB models are based on natural processes,which enhances their interpretability and generality.However,they require a high level of research and testing,which can be costly.DD models rely on artificial intelligence methods such as machine learning,which offer flexible and diverse modeling approaches.However,they depend on data quality,lack mechanism support,and are location-specific.Both models have been extensively studied in the past decades and have been applied in some lakes and reservoirs.To further improve model performance,future research should improve the frequency and quality of data monitoring and combine natural process mechanisms with artificial intelligence methods.

lakes and reservoirsalgal bloomsinfluencing factorsalgae concentration predictionprocess-based modelsdata-driven models

谢宇煊、汪隽、唐雨青、朱芸、田泽辉、周达诚、陈超

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清华大学环境学院 北京 100084

唐山市自来水公司 唐山 063000

苏州科技大学环境科学与工程学院 苏州 215009

香港中文大学 香港 999077

清华苏州环境创新研究院 苏州 215163

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湖库水源地 藻类水华 影响因子 藻类浓度预测 过程机理模型 数据驱动模型

山东省重点研发计划项目国家自然科学基金项目成都市重点河流"一河一策一图一单"分析及应急演练服务项目

2020CXGC01140622076091N5101012023000142-1

2024

10.7536/PC240313

化学进展
中国科学院基础科学局,化学部,文献情报中心 国家自然科学基金委员会化学科学部

化学进展

CSTPCD北大核心
影响因子：1.079
ISSN：1005-281X
年,卷(期)：2024.36(9)