An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

被引:6
|
作者
Qian, Jing [1 ,2 ]
Qian, Li [3 ]
Pu, Nan [4 ]
Bi, Yonghong [5 ]
Wilhelms, Andre [1 ]
Norra, Stefan [1 ,6 ]
机构
[1] Karlsruhe Inst Technol, Inst Appl Geosci, D-76131 Karlsruhe, Germany
[2] China Railway Hitech Ind Co Ltd, Beijing 100070, Peoples R China
[3] Ludwig Maximilian Univ Munich, Inst Informat, D-80538 Munich, Germany
[4] Leiden Univ, Inst Adv Comp Sci, NL-2333 CA Leiden, Netherlands
[5] Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China
[6] Potsdam Univ, Inst Environm Sci & Geog, Soil Sci & Geoecol, D-14476 Potsdam Golm, Germany
关键词
HABs; Chl-a; Bloomformer-2; earlywarning; time-series analysis; CHLOROPHYLL-A; LAKE TAIHU; GENETIC ALGORITHM; WATER; PREDICTION; MACHINE; MODELS;
D O I
10.1021/acs.est.3c03906
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order to develop an intelligent early warning system for HABs, big data and deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring system (VAMS). Subsequently, the analysis and stratification of the vertical aquatic layer were conducted employing the "DeepDPM-Spectral Clustering" method. This approach drastically reduced the number of predictive models and enhanced the adaptability of the system. The Bloomformer-2 model was developed to conduct both single-step and multistep predictions of Chl-a, integrating the " Alert Level Framework" issued by the World Health Organization to accomplish early warning for HABs. The case study conducted in Taihu Lake revealed that during the winter of 2018, the water column could be partitioned into four clusters (Groups W1-W4), while in the summer of 2019, the water column could be partitioned into five clusters (Groups S1-S5). Moreover, in a subsequent predictive task, Bloomformer-2 exhibited superiority in performance across all clusters for both the winter of 2018 and the summer of 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, and MAPE: 0.228-2.279 for single-step prediction; MAE: 0.184-0.505, MSE: 0.101-0.378, and MAPE: 0.243-4.011 for multistep prediction). The prediction for the 3 days indicated that Group W1 was in a Level I alert state at all times. Conversely, Group S1 was mainly under an Level I alert, with seven specific time points escalating to a Level II alert. Furthermore, the end-to-end architecture of this system, coupled with the automation of its various processes, minimized human intervention, endowing it with intelligent characteristics. This research highlights the transformative potential of integrating big data and artificial intelligence in environmental management and emphasizes the importance of model interpretability in machine learning applications.
引用
收藏
页码:15607 / 15618
页数:12
相关论文
共 36 条
  • [31] Implementation of an early warning system with hyperspectral imaging combined with deep learning model for chlorine in refuse derived fuels
    Ozkan, Metin
    Ozkan, Kemal
    Bekgoz, Baki Osman
    Yorulmaz, Ozge
    Guenkaya, Zerrin
    Ozkan, Aysun
    Banar, Muefide
    WASTE MANAGEMENT, 2022, 142 : 111 - 119
  • [32] An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System
    Hayder, Israa M. M.
    Al-Amiedy, Taief Alaa
    Ghaban, Wad
    Saeed, Faisal
    Nasser, Maged
    Al-Ali, Ghazwan Abdulnabi
    Younis, Hussain A. A.
    PROCESSES, 2023, 11 (02)
  • [33] Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review
    Alam, Md Ashraful
    Sajib, Md Refat Uz Zaman
    Rahman, Fariya
    Ether, Saraban
    Hanson, Molly
    Sayeed, Abu
    Akter, Ema
    Nusrat, Nowrin
    Islam, Tanjeena Tahrin
    Raza, Sahar
    Tanvir, K. M.
    Chisti, Mohammod Jobayer
    Rahman, Qazi Sadeq-ur
    Hossain, Akm
    Layek, Ma
    Zaman, Asaduz
    Rana, Juwel
    Rahman, Syed Moshfiqur
    El Arifeen, Shams
    Rahman, Ahmed Ehsanur
    Ahmed, Anisuddin
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2024, 26
  • [34] Early warning research on enterprise carbon emission reduction credit risk based on deep learning model under unbalanced data
    Long, Zhi
    Chen, Xiangzhou
    FRONTIERS IN ENERGY RESEARCH, 2023, 11
  • [35] A Bayesian Deep Learning-Based Probabilistic Risk Assessment and Early-Warning Model for Power Systems Considering Meteorological Conditions
    Liu, Xiaoming
    Liu, Jun
    Zhao, Yu
    Ding, Tao
    Liu, Xinglei
    Liu, Jiacheng
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2024, 20 (02) : 1516 - 1527
  • [36] Using Deep Learning for Rapid Earthquake Parameter Estimation in Single-Station Single-Component Earthquake Early Warning System
    Abdalzaher, Mohamed S.
    Sami Soliman, M. Sami
    Fouda, Mostafa M.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62