Abstract
The prediction of harmful algal blooms is most important because harmful algal blooms are having a high impact on marine ecosystems and human health. In this research, proposed a method for predicting harmful algal blooms (HABs) using the light gradient boost machine (LightGBM) algorithm. The proposed method utilizes metadata and satellite imagery from NASA to forecast when HABs will arise, with the root mean square error (RMSE) used to evaluate the accuracy of the prediction algorithm. The method achieves high accuracy and low false alarm rates compared to existing boosting algorithms, namely XGBoost, CatBoost, and CNN. Experimental results are conducted in order to identify harmful algal species. These findings demonstrate the effectiveness of using boosting algorithms and metadata for HAB prediction, which enable more effective management of the harmful impacts of HAB.
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References
Bretz CK, Manouki TJ, Kvitek RG (2002) Emerita analoga (Stimpson) as an indicator species for paralytic shellfish poisoning toxicity along the California coast. Toxicon 40(8):1189–1196
Anderson DM, Fensin E, Gobler CJ, Hoeglund AE, Hubbard KA, Kulis DM, Landsberg JH et al. (2021) Marine harmful algal blooms (HABs) in the United States: history, current status and future trends. Harmful Algae 102:101975
Ghatkar JG, Singh RK, Shanmugam P (2019) Classification of algal bloom species from remote sensing data using an extreme gradient boosted decision tree model. Int J Remote Sens 40(24):9412–9438
DrivenData runs online machine learning competitions with social impact and works directly with mission-driven organizations to drive change through data science and engineering. It provides the datasets for the problems. Tick Tick Bloom competion https://drivendata.co/blog/tick-tick-bloom-benchmark
Image courtesy of NASA Earth Observatory, Joshua Stevens, using Landsat imagery from NASA/USGS. Depicts a 2017 algal bloom in Lake Erie., Tick Tick Bloom: Harmful Algal Bloom Detection Challenge
Martinez-Vicente V, Kurekin A, Sá C, Brotas V, Amorim A, Veloso V, Lin J, Miller PI (2020) Sensitivity of a satellite algorithm for harmful algal bloom discrimination to the use of laboratory bio-optical data for training. Front Mar Sci 7:582960
Karki S, Sultan M, Elkadiri R, Elbayoumi T (2018) Map** and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida. Remote Sensing 10(10):1656
Fauziah SH, Rizman-Idid M, Cheah W, Loh K-H, Sharma S, NoorMaiza MR, Bordt M et al. (2021) Marine debris in Malaysia: a review on the pollution intensity and mitigating measures. Marine Pollution Bulletin 167:112258
Balakrishna G, Durbha SS, King RL, Younan NH (2009) Sensor web and data mining approaches for harmful algal bloom detection and monitoring in the gulf of Mexico region. In: 2009 IEEE international geoscience and remote sensing symposium, vol 3. IEEE, pp III-789
Zhang F, Wang Y, Cao M, Sun X, Zhenhong D, Liu R, Ye X (2016) Deep-learning-based approach for prediction of algal blooms. Sustainability 8(10):1060
Mohammed SAS (2020) Machine learning in algal bloom detection final thesis
Zheng L, Wang H, Liu C, Zhang S, Ding A, **e E, Li J, Wang S (2021) Prediction of harmful algal blooms in large water bodies using the combined EFDC and LSTM models. J Environ Manage 295:113060
Aranay OM, Atrey PK (2022) Deep active genetic learning-based assessment of lakes’ water quality using climate data. IEEE Trans Sustain Comput 7(4):851–863
Baek S-S, Pyo JC, Kwon YS, Chun S-J, Baek SH, Ahn C-Y, Oh H-M, Kim YO, Cho KH (2021) Deep learning for simulating harmful algal blooms using ocean numerical model. Front Marine Sci 8:729954
Wen J, Yang J, Li Y, Gao L (2022) Harmful algal bloom warning based on machine learning in maritime site monitoring. Knowl-Based Syst 245:108569
Ke Y, Dai Y, Xu M, Mo Y (2019) Tunnel surface settlement forecasting with ensemble learning. Sustainability 12(1):232
Hill PR, Kumar A, Temimi M, Bull DR (2020) HABNet: Machine learning, remote sensing-based detection of harmful algal blooms. IEEE J Selected Topics in Appl Earth Observ Remote Sens 13:3229–3239
**u L, Yu J, Jia Z, Song J (2014) Harmful algal blooms prediction with machine learning models in Tolo harbour. In: 2014 International conference on smart computing, IEEE, pp 245–250
Yu P, Gao R, Zhang D, Liu Z-P (2021) Predicting coastal algal blooms with environmental factors by machine learning methods. Ecol Ind 123:107334
Moein I, Sultan M, Kadiri RE, Ghannadi A, Abdelmohsen K (2021) A remote sensing and machine learning-based approach to forecast the onset of harmful algal bloom. Remote Sens 13(19):3863
Kwon DH, Hong SM, Abbas A, Pyo JC, Lee H-K, Baek S-S, Cho KH (2023) Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning. Environ Eng Res 28(1)
Ly QV, Nguyen XC, Lê NC, Truong T-D, Hoang T-HT, Park TJ, Maqbool T et al. (2021) Application of machine learning for eutrophication analysis and algal bloom prediction in an urban river: a 10-year study of the Han River, South Korea. Sci The Total Environ 797:149040
Lemos AT, Ghisolfi RDR, Mazzini PLF (2018) Annual phytoplankton blooming using satellite-derived chlorophyll-a data around the Vitória-Trindade Chain, Southeastern Brazil. Deep Sea Res Part I 136:62–71
Feng C, Wang S, Li Z (2022) Long-term spatial variation of algal blooms extracted using the U-net model from 10 years of GOCI imagery in the East China Sea. J Environ Manage 321:115966
Kumar ACS, Bhandarkar SM (2017) A deep learning paradigm for detection of harmful algal blooms. In: 2017 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 743–751
Yi H-S, Lee B, Park S, Kwak K-C, An K-G (2019) Prediction of short-term algal bloom using the M5P model-tree and extreme learning machine. Environ Eng Res 24(3):404–411
Balakrishna G, Durbha SS, King RL, Younan NH (2011) Investigation of evolutionary feature subset selection in multi-temporal datasets for harmful algal bloom detection. In: 2011 6th International workshop on the analysis of multi-temporal remote sensing images (Multi-Temp), IEEE, pp 149–152
Yerrapothu, Bala Tripura Sundari. “Application of Machine Learning Techniques to Forecast Harmful Algal Blooms in Gulf of Mexico.“ (2021)
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Karthikeyan, N., Bhargav, M., krishna, S.H., Madhav, Y.S., Sajana, T. (2024). Prediction of Harmful Algal Blooms Severity Using Machine Learning and Deep Learning Techniques. In: Jacob, I.J., Piramuthu, S., Falkowski-Gilski, P. (eds) Data Intelligence and Cognitive Informatics. ICDICI 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-7962-2_34
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DOI: https://doi.org/10.1007/978-981-99-7962-2_34
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