Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine
Abstract Lakes and reservoirs are critical for sustaining water resources, serving as key sources of water supply. Monitoring changes in lake properties offers valuable insight into water resource management, agricultural demand, watershed analysis, and environmental monitoring. This study introduce...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Water |
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| Online Access: | https://doi.org/10.1007/s43832-025-00255-5 |
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| author | Golmar Golmohammadi Hamid Reza Eslami Rashid Rasouli Harouni Kamyab Eslami Mahin Jamali Seyed Mostafa Biazar Kourosh Mohammadi |
| author_facet | Golmar Golmohammadi Hamid Reza Eslami Rashid Rasouli Harouni Kamyab Eslami Mahin Jamali Seyed Mostafa Biazar Kourosh Mohammadi |
| author_sort | Golmar Golmohammadi |
| collection | DOAJ |
| description | Abstract Lakes and reservoirs are critical for sustaining water resources, serving as key sources of water supply. Monitoring changes in lake properties offers valuable insight into water resource management, agricultural demand, watershed analysis, and environmental monitoring. This study introduces an innovative approach that utilizes the Google Earth Engine platform, artificial intelligence (AI), and genetic algorithms to analyze water surface areas and estimate lake volume without the need for bathymetric data. The method was applied to Lake Okeechobee in Florida, Using Landsat-8 imagery and the normalized difference water index to calculate the lake’s surface Area. Ai techniques, including image segmentation and thresholding, were employed to refine the images. These processed images were then analyzed using a genetic algorithm to estimate the lake’s volume. The estimated volume was compared with calculations derived from the lake’s bathymetry, achieving a root mean square error of 273 million cubic meters (Mm3), a mean absolute percentage error of 28.85%, and a percent bias of 21.8%. These findings suggest that AI techniques can be highly useful for estimating lake volumes, and further exploration of their application is recommended in future studies. |
| format | Article |
| id | doaj-art-2c1f9ac6b8b64608b95f7ea36f860423 |
| institution | Kabale University |
| issn | 2730-647X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Water |
| spelling | doaj-art-2c1f9ac6b8b64608b95f7ea36f8604232025-08-20T04:02:55ZengSpringerDiscover Water2730-647X2025-08-015112610.1007/s43832-025-00255-5Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth EngineGolmar Golmohammadi0Hamid Reza Eslami1Rashid Rasouli Harouni2Kamyab Eslami3Mahin Jamali4Seyed Mostafa Biazar5Kourosh Mohammadi6Department of Soil, Water and Ecosystem Sciences, IFAS/RCREC, University of FloridaPayeshgar Tadbir Afzar (Knowledgebase Company)Payeshgar Tadbir Afzar (Knowledgebase Company)Payeshgar Tadbir Afzar (Knowledgebase Company)Payeshgar Tadbir Afzar (Knowledgebase Company)Department of Soil, Water and Ecosystem Sciences, IFAS/RCREC, University of FloridaDepartment of Water Resources Engineering, Planet EngineeringAbstract Lakes and reservoirs are critical for sustaining water resources, serving as key sources of water supply. Monitoring changes in lake properties offers valuable insight into water resource management, agricultural demand, watershed analysis, and environmental monitoring. This study introduces an innovative approach that utilizes the Google Earth Engine platform, artificial intelligence (AI), and genetic algorithms to analyze water surface areas and estimate lake volume without the need for bathymetric data. The method was applied to Lake Okeechobee in Florida, Using Landsat-8 imagery and the normalized difference water index to calculate the lake’s surface Area. Ai techniques, including image segmentation and thresholding, were employed to refine the images. These processed images were then analyzed using a genetic algorithm to estimate the lake’s volume. The estimated volume was compared with calculations derived from the lake’s bathymetry, achieving a root mean square error of 273 million cubic meters (Mm3), a mean absolute percentage error of 28.85%, and a percent bias of 21.8%. These findings suggest that AI techniques can be highly useful for estimating lake volumes, and further exploration of their application is recommended in future studies.https://doi.org/10.1007/s43832-025-00255-5Lake bathymetryRemote sensingArtificial intelligenceGenetic algorithm for volume estimationLake Okeechobee. Landsate-8Image segmentation |
| spellingShingle | Golmar Golmohammadi Hamid Reza Eslami Rashid Rasouli Harouni Kamyab Eslami Mahin Jamali Seyed Mostafa Biazar Kourosh Mohammadi Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine Discover Water Lake bathymetry Remote sensing Artificial intelligence Genetic algorithm for volume estimation Lake Okeechobee. Landsate-8 Image segmentation |
| title | Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine |
| title_full | Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine |
| title_fullStr | Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine |
| title_full_unstemmed | Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine |
| title_short | Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine |
| title_sort | estimating lake okeechobee s water volume using ndwi derived remote sensing and genetic algorithm optimization on google earth engine |
| topic | Lake bathymetry Remote sensing Artificial intelligence Genetic algorithm for volume estimation Lake Okeechobee. Landsate-8 Image segmentation |
| url | https://doi.org/10.1007/s43832-025-00255-5 |
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