Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans
This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025007844 |
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| author | Sheikh Fahim Faysal Sowrav Sujit Kumar Debsarma Mohan Kumar Das Khan Mohammad Ibtehal Mahfujur Rahman Noshin Tabassum Hridita Atika Afia Broty Muhammad Sajid Anam Hoque |
| author_facet | Sheikh Fahim Faysal Sowrav Sujit Kumar Debsarma Mohan Kumar Das Khan Mohammad Ibtehal Mahfujur Rahman Noshin Tabassum Hridita Atika Afia Broty Muhammad Sajid Anam Hoque |
| author_sort | Sheikh Fahim Faysal Sowrav |
| collection | DOAJ |
| description | This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH—were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy. Comparative analyses showed that ML-based models effectively captured spatial and temporal variations, aligning closely with field measurements. This integration provides a more efficient alternative to traditional methods, which are resource-intensive and less practical for large-scale, remote areas.Our findings demonstrate that this semi-automated technique is a valuable tool for continuous water quality monitoring, particularly in ecologically sensitive areas with limited accessibility. The approach also offers significant applications for climate resilience and policy-making, as it enables timely identification of deteriorating water quality trends that may impact biodiversity and ecosystem health. However, the study acknowledges limitations, including the variability in data availability and the inherent uncertainties in ML predictions for dynamic water systems. Overall, this research contributes to the advancement of water quality monitoring techniques, supporting sustainable environmental management practices and the resilience of the Sundarbans against emerging climate challenges. |
| format | Article |
| id | doaj-art-28262a33802f411782a76b56f465379d |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-28262a33802f411782a76b56f465379d2025-02-04T04:10:31ZengElsevierHeliyon2405-84402025-02-01113e42404Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for SundarbansSheikh Fahim Faysal Sowrav0Sujit Kumar Debsarma1Mohan Kumar Das2Khan Mohammad Ibtehal3Mahfujur Rahman4Noshin Tabassum Hridita5Atika Afia Broty6Muhammad Sajid Anam Hoque7National Oceanographic And Maritime Institute (NOAMI), Bangladesh; Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET), Bangladesh; Bangabandhu Sheikh Mujibur Rahman Maritime University, Bangladesh; Corresponding author. National Oceanographic And Maritime Institute (NOAMI), Bangladesh.National Oceanographic And Maritime Institute (NOAMI), BangladeshNational Oceanographic And Maritime Institute (NOAMI), Bangladesh; South Asian Meteorological Association (SAMA), BangladeshNational Oceanographic And Maritime Institute (NOAMI), BangladeshInstitute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET), BangladeshNational Oceanographic And Maritime Institute (NOAMI), BangladeshNational Oceanographic And Maritime Institute (NOAMI), Bangladesh; Bangabandhu Sheikh Mujibur Rahman Maritime University, BangladeshNational Oceanographic And Maritime Institute (NOAMI), Bangladesh; Bangabandhu Sheikh Mujibur Rahman Maritime University, BangladeshThis study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH—were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy. Comparative analyses showed that ML-based models effectively captured spatial and temporal variations, aligning closely with field measurements. This integration provides a more efficient alternative to traditional methods, which are resource-intensive and less practical for large-scale, remote areas.Our findings demonstrate that this semi-automated technique is a valuable tool for continuous water quality monitoring, particularly in ecologically sensitive areas with limited accessibility. The approach also offers significant applications for climate resilience and policy-making, as it enables timely identification of deteriorating water quality trends that may impact biodiversity and ecosystem health. However, the study acknowledges limitations, including the variability in data availability and the inherent uncertainties in ML predictions for dynamic water systems. Overall, this research contributes to the advancement of water quality monitoring techniques, supporting sustainable environmental management practices and the resilience of the Sundarbans against emerging climate challenges.http://www.sciencedirect.com/science/article/pii/S2405844025007844Surface water qualitySundarbansGoogle earth engine (GEE)Machine learning (ML)Chlorophyll-aEutrophication |
| spellingShingle | Sheikh Fahim Faysal Sowrav Sujit Kumar Debsarma Mohan Kumar Das Khan Mohammad Ibtehal Mahfujur Rahman Noshin Tabassum Hridita Atika Afia Broty Muhammad Sajid Anam Hoque Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans Heliyon Surface water quality Sundarbans Google earth engine (GEE) Machine learning (ML) Chlorophyll-a Eutrophication |
| title | Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans |
| title_full | Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans |
| title_fullStr | Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans |
| title_full_unstemmed | Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans |
| title_short | Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans |
| title_sort | developing a semi automated technique of surface water quality analysis using gee and machine learning a case study for sundarbans |
| topic | Surface water quality Sundarbans Google earth engine (GEE) Machine learning (ML) Chlorophyll-a Eutrophication |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025007844 |
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