Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction
This study advances environmental monitoring by predicting the spatial and temporal distribution of Harmful Algal Blooms (HABs) in the Republic of Korea through a hybrid approach that combines geostatistical and deep learning methods. Using 3D universal kriging, the study interpolates missing HAB co...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Communications |
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| Online Access: | https://doi.org/10.1088/2515-7620/adae5d |
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| _version_ | 1823856601421840384 |
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| author | Sung Jae Kim Yongbok Cho |
| author_facet | Sung Jae Kim Yongbok Cho |
| author_sort | Sung Jae Kim |
| collection | DOAJ |
| description | This study advances environmental monitoring by predicting the spatial and temporal distribution of Harmful Algal Blooms (HABs) in the Republic of Korea through a hybrid approach that combines geostatistical and deep learning methods. Using 3D universal kriging, the study interpolates missing HAB concentration values, transforming geospatial point data into spatially continuous grid images that serve as the foundation for predictive modeling. These interpolated images are then used as input for a ConvLSTM (Convolutional Long Short-Term Memory) network, which integrates convolutional layers to capture spatial patterns and LSTM units to model temporal dependencies. By leveraging this spatiotemporal modeling framework, the ConvLSTM network effectively predicts future HAB concentrations with improved accuracy. This innovative methodology highlights the utility of combining 3D universal kriging for spatial interpolation with image-based ConvLSTM prediction, offering valuable insights into HAB dynamics and supporting sustainable strategies for environmental management and public health. |
| format | Article |
| id | doaj-art-8aa9feda94ff49f78eb18c0dd9c9fd51 |
| institution | Kabale University |
| issn | 2515-7620 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Communications |
| spelling | doaj-art-8aa9feda94ff49f78eb18c0dd9c9fd512025-02-12T06:14:03ZengIOP PublishingEnvironmental Research Communications2515-76202025-01-017202501210.1088/2515-7620/adae5dEnhancing environmental monitoring of harmful algal blooms with ConvLSTM image predictionSung Jae Kim0https://orcid.org/0009-0008-5497-0412Yongbok Cho1https://orcid.org/0000-0001-9496-5898Department of Management Information Systems College of Business Administration, Dong-A University , Busan 49236, Republic of KoreaDepartment of Management Information Systems College of Business Administration, Dong-A University , Busan 49236, Republic of KoreaThis study advances environmental monitoring by predicting the spatial and temporal distribution of Harmful Algal Blooms (HABs) in the Republic of Korea through a hybrid approach that combines geostatistical and deep learning methods. Using 3D universal kriging, the study interpolates missing HAB concentration values, transforming geospatial point data into spatially continuous grid images that serve as the foundation for predictive modeling. These interpolated images are then used as input for a ConvLSTM (Convolutional Long Short-Term Memory) network, which integrates convolutional layers to capture spatial patterns and LSTM units to model temporal dependencies. By leveraging this spatiotemporal modeling framework, the ConvLSTM network effectively predicts future HAB concentrations with improved accuracy. This innovative methodology highlights the utility of combining 3D universal kriging for spatial interpolation with image-based ConvLSTM prediction, offering valuable insights into HAB dynamics and supporting sustainable strategies for environmental management and public health.https://doi.org/10.1088/2515-7620/adae5dharmful algal blooms3D universal krigingspatiotemporal modelimage predictiondeep learning |
| spellingShingle | Sung Jae Kim Yongbok Cho Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction Environmental Research Communications harmful algal blooms 3D universal kriging spatiotemporal model image prediction deep learning |
| title | Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction |
| title_full | Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction |
| title_fullStr | Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction |
| title_full_unstemmed | Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction |
| title_short | Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction |
| title_sort | enhancing environmental monitoring of harmful algal blooms with convlstm image prediction |
| topic | harmful algal blooms 3D universal kriging spatiotemporal model image prediction deep learning |
| url | https://doi.org/10.1088/2515-7620/adae5d |
| work_keys_str_mv | AT sungjaekim enhancingenvironmentalmonitoringofharmfulalgalbloomswithconvlstmimageprediction AT yongbokcho enhancingenvironmentalmonitoringofharmfulalgalbloomswithconvlstmimageprediction |