An improved method for water depth mapping in turbid waters based on a machine learning model

Liang, Yitao; Cheng, Zhixin; Du, Yixiao; Song, Dehai; You, Zaijin

doi:10.1016/j.ecss.2023.108577

Cited by 3 publications

(4 citation statements)

References 42 publications

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“…Most of the published studies involving Landsat images use simple approaches of either original bands or the band ratio coupled with linear models (i.e., Stumpf model [24], Generalized additive model (GAM) [23], Lyzenga optical model [19] or common ML models [27]). While this approach showed promising confidence in water depth estimation in clear coastal and ocean waters, there was a great variation in accuracy in inland turbid waters (e.g., river [22], estuary [28]).…”

Section: Discussionmentioning

confidence: 99%

“…Fewer studies were found for bathymetry mapping in turbid water (i.e., rivers and lagoons) using Landsat imagery. We found an optimal band ratio approach coupled with Landsat 9 [22] and a fused model of Adaboost and XGB (Adaboost-XGB) integrated into Landsat 8 [28] to derive the depth map in turbid water, all of which enhanced but varied the model confidence to R 2 = 0.86 and 0.97, respectively. Liang et al 2024 [28] implemented the fused Adaboost-XGB in a mixed area of clear and turbid water, while Niroumand-Jadidi et al 2021 [22] deployed the retrieval model in different turbidity conditions but with a large variation in the coefficient of determination (0.44 -0.86), leaving uncertainty in estimated depth in shallow and turbid waters.…”

Section: Introductionmentioning

confidence: 99%

“…We found an optimal band ratio approach coupled with Landsat 9 [22] and a fused model of Adaboost and XGB (Adaboost-XGB) integrated into Landsat 8 [28] to derive the depth map in turbid water, all of which enhanced but varied the model confidence to R 2 = 0.86 and 0.97, respectively. Liang et al 2024 [28] implemented the fused Adaboost-XGB in a mixed area of clear and turbid water, while Niroumand-Jadidi et al 2021 [22] deployed the retrieval model in different turbidity conditions but with a large variation in the coefficient of determination (0.44 -0.86), leaving uncertainty in estimated depth in shallow and turbid waters. This accuracy variation may be attributed to the limited number of input features (i.e., only original bands and band ratios) used in the published models, leaving a gap where popular image feature extraction is not utilized [29], such as gray scale morphological operation (GSMO) and morphological multi-scale decomposition (MMSD), to improve the accuracy using multi-dimensional estimation models.…”

Section: Introductionmentioning

confidence: 99%

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Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Tran,

Nguyen,

Pham

et al. 2024

Geosciences

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Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Tran,

Nguyen,

Pham

et al. 2024

Geosciences

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“…This degradation in light induces alterations in the spectral properties of the satellite imagery and LiDAR sensors, potentially leading to diminished image contrast. Consequently, this can compromise the precision of mapping shallow water depths, as the accuracy in delineating these regions is adversely impacted, with high turbidity levels [37].…”

Section: Challenges Of the Hydrographic Surveys In The Study Areamentioning

confidence: 99%

Innovative Maritime Uncrewed Systems and Satellite Solutions for Shallow Water Bathymetric Assessment

Constantinoiu,

Tavares,

Cândido

et al. 2024

Inventions

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Shallow water bathymetry is a topic of significant interest in various fields, including civil construction, port monitoring, and military operations. This study presents several methods for assessing shallow water bathymetry using maritime uncrewed systems (MUSs) integrated with advanced and innovative sensors such as Light Detection and Ranging (LiDAR) and multibeam echosounder (MBES). Furthermore, this study comprehensively describes satellite-derived bathymetry (SDB) techniques within the same geographical area. Each technique is thoroughly outlined with respect to its implementation and resultant data, followed by an analytical comparison encompassing their accuracy, precision, rapidness, and operational efficiency. The accuracy and precision of the methods were evaluated using a bathymetric reference survey conducted with traditional means, prior to the MUS survey and with cross-comparisons between all the approaches. In each assessment of the survey methodologies, a comprehensive evaluation is conducted, explaining both the advantages and limitations for each approach, thereby enabling an inclusive understanding for the reader regarding the efficacy and applicability of these methods. The experiments were conducted as part of the Robotic Experimentation and Prototyping using Maritime Unmanned Systems 23 (REPMUS23) multinational exercise, which was part of the Rapid Environmental Assessment (REA) experimentations.

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The Integration of Bird Swarm Algorithm and Segmental Ensemble a Novel Intelligent Approach for Water Depth Inversion

Wu,

Zhao,

et al. 2024

Preprint

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An improved method for water depth mapping in turbid waters based on a machine learning model

Cited by 3 publications

References 42 publications

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Innovative Maritime Uncrewed Systems and Satellite Solutions for Shallow Water Bathymetric Assessment

The Integration of Bird Swarm Algorithm and Segmental Ensemble a Novel Intelligent Approach for Water Depth Inversion

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