A Comparative Analysis of Data-Driven Modeling Approaches to Forecast Cyanobacteria Algal Blooms in Eutrophic Lake Discharge Canals.

Hung Q Nguyen,Mauricio E Arias,Qiong Zhang, Osama M Tarabih, Cassondra Armstrong, Detong Sun,Edward J Phlips

Journal of environmental management（2025）

South Florida Water Management District

Cited 0|Views2

Abstract

This study explored the use of data-driven models to develop management-oriented prediction tools for algae blooms (ABs) represented by Chlorophyll-A (Chla) concentrations, using the Caloosahatchee and St. Lucie canals in Lake Okeechobee, Florida, as case studies. By comparing two modeling approaches, i.e., cascading modeling and time-lag modeling, the study aims to understand the differences in Chla dynamics between the two canals, identify the main drivers and predictors of Chla concentration in each, and develop suitable forecasting models for the canals' operation purposes. Throughout this study, both approaches demonstrated their value in improving the understanding of water quality dynamics in Lake Okeechobee canals. While some water quality parameters such as Dissolved Oxygen (DO) and Nitrate-Nitrite (NOx) were critical to ABs in the Caloosahatchee and St. Lucie canals, respectively, the effect of operation decisions on ABs was more significant on the St. Lucie than on the Caloosahatchee. From a modeling perspective, the time-lag modeling approach achieved higher predictive accuracy for Chla concentrations in both Caloosahatchee and St. Lucie canals. Particularly, at station S80 of St. Lucie canal, the XGBoost (XGB) algorithm achieved R2= 99% and RMSE = 0.001 μg/l in training, and R2= 60.1% and RMSE = 4.58 μg/l in testing. At station S79 of Caloosahatchee canal, Random Forest (RF) appeared to be the best model with R2= 85.7% and RMSE = 5.63 μg/l in training, and R2= 39% with RMSE = 10.06 μg/l in testing. In this study, the time-lag modeling approach was proven to offer decision-makers flexible tools for implementing better management strategies based solely on operation decisions and meteorological conditions.

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

【要点】：本研究通过比较级联模型和时间滞后模型两种数据驱动方法，预测佛罗里达州奥基乔比湖的卡洛萨哈切运河和圣露西亚运河中藻类水华（以叶绿素A浓度代表），揭示了两种运河中Chla动态的差异，并开发了适合运河运营的预测模型。

【方法】：研究通过级联模型和时间滞后模型对藻类水华的主要驱动因素和预测因子进行分析，比较了两种模型在预测Chla浓度方面的性能。

【实验】：实验使用了卡洛萨哈切运河的S79站和圣露西亚运河的S80站的数据，数据集名称未在摘要中提及。结果显示，时间滞后模型在两个运河的Chla浓度预测中均展现出更高的准确性，特别是在圣露西亚运河的S80站，XGBoost算法在训练阶段达到了R2=99%和RMSE=0.001μg/l，在测试阶段达到了R2=60.1%和RMSE=4.58μg/l；而在卡洛萨哈切运河的S79站，随机森林模型在训练阶段表现最佳，R2=85.7%和RMSE=5.63μg/l，在测试阶段R2=39%和RMSE=10.06μg/l。