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.
关键词
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
Key words
lakes and reservoirs/algal blooms/influencing factors/algae concentration prediction/process-based models/data-driven models
维普
万方数据