Estimating lake okeechobee’s water volume using NDWI-derived remote sensing and genetic algorithm optimization on Google Earth Engine

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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Bibliographic Details
Main Authors: Golmar Golmohammadi, Hamid Reza Eslami, Rashid Rasouli Harouni, Kamyab Eslami, Mahin Jamali, Seyed Mostafa Biazar, Kourosh Mohammadi
Format: Article
Language:English
Published: Springer 2025-08-01
Series:Discover Water
Subjects:
Lake bathymetry
Remote sensing
Artificial intelligence
Genetic algorithm for volume estimation
Lake Okeechobee. Landsate-8
Image segmentation
Online Access:https://doi.org/10.1007/s43832-025-00255-5
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Summary: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.
ISSN:2730-647X

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