On improved nearshore bathymetry estimates from satellites using ensemble and machine learning approaches

Kumar, Surisetty V. V. Arun; Venkateswarlu, Ch.; Gireesh, B.; Prasad, K. Venkatesh; Sharma, Rashmi

doi:10.1016/j.asr.2021.06.034

Cited by 15 publications

(7 citation statements)

References 62 publications

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“…The optimal hyperparameters of the KNN model are presented in Table 1. The SVM model, with the radial basis function (RBF) kernel, can effectively address minor sample problems and establish a reliable relationship between satellite imagery and water depth [16,58,59]. Furthermore, the nonlinear kernel function of SVM, which transforms the training set into a high-dimensional feature space, enhances the generalization capacity.…”

Section: Modelsmentioning

confidence: 99%

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Cheng,

Chu

et al. 2023

IJGI

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Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry contributes to formulating reliable bathymetric strategies, particularly for areas such as the Spratly Islands, where in situ observations are exceptionally scarce. In this study, we took Anda Reef as a case study and evaluated the performance of eight common SDB approaches by integrating Sentinel-2 images with Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The bathymetric maps were generated using two classical and six machine-learning algorithms, which were then validated with measured sonar data. The results illustrated that all models accurately estimated the depth of coral reefs in the 0–20 m range. The classical algorithms (Lyzenga and Stumpf) exhibited a mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of less than 0.990 m, 1.386 m, and 11.173%, respectively. The machine learning algorithms generally outperformed the classical algorithms in accuracy and bathymetric detail, with a coefficient of determination (R2) ranging from 0.94 to 0.96 and an RMSE ranging from 1.034 m to 1.202 m. The multilayer perceptron (MLP) achieved the highest accuracy and consistency with an RMSE of as low as 1.034 m, followed by the k-nearest neighbor (KNN) (1.070 m). Our results provide a practical reference for selecting SDB algorithms to accurately obtain shallow water bathymetry in subsequent studies.

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Section: Modelsmentioning

confidence: 99%

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Cheng,

Chu

et al. 2023

IJGI

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“…In, ML models have been applied to other environmental remote sensing applications such as landslide monitoring/prediction, estimating nearshore water depths, weather forecast by monitoring and forecasting precipitable water vapor (PWV), and forecast hourly intense rainfall [11,[55][56][57][58][59][60][61][62].…”

Section: Earth Observation and Monitoringmentioning

confidence: 99%

“…7). The ML models have been compared with several conventional non-ML models: regression model [14,80,101,159,160], brute force approach [143], traditional statistical approaches [60,94,[161][162][163][164], classical KF [129], Bayes-optimal rule [118], least square (LS)-based approach [40], Saastamoinen model [110], autoregressive model and a traditional LEO propagation model (EKF-STAN) [146], conventional wind speed retrieval method [43], Maximum-Likelihood Power-Distortion (PD-ML) [165], BERNESE 5.2 [114], CYGNSS [44], Hydrostaticseasonal-time (HST) model [49], Statistical Theta method [51][52][53]166], MAPGEO2004 geoid model [73], GNSS-IR soil moisture [58], Autoregressive (AR) and Autoregressive Moving Average (ARMA) [167], ERA-Interima global atmospheric reanalysis (now ERA5 reanalysis) [107], Empirical linear algorithms (LRM and LLM) [59], International Reference Ionosphere (IRI) 2016 model [168], NeQuick and IRI-2001 global TEC model [169][170][171], EKF-based integration scheme [172], CODE GIMs (Global Ionospheric Maps) [173], autoregressive integrated moving average (ARIMA), and quadratic polynomial (QP) models [174], least square regression algorithms (LSR) and bi-ha...…”

Section: E ML Vs Non-ml Models (Rq4a)mentioning

confidence: 99%

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Siemuri

Selvan

Välisuo

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. The aim of this research is to perform a systematic review of the type of ML models utilized in GNSS use cases, their performance with respect to accuracy, their comparison with other models (ML and non-ML), and their GNSS application context. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning

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“…While empirical equations, predominantly derived from laboratory experiments, aim to understand intercorrelations between major morphometrics and hydrodynamic conditions, improved ML models with extensive hyperparameters have showcased potential in capturing these intricate natural behaviors. The integration of ML in coastal and ocean engineering is currently flourishing, yielding results suggesting higher accuracies in capturing complex relations [18][19][20][21] . At the same time, recent developments in data observation techniques, such as remote sensing, have generated large-scale datasets across various scientific disciplines.…”

Section: Introductionmentioning

confidence: 99%

Beach resilience to storm-induced erosion: a machine learning-driven approach to identify key morphometrics to recovery

Thilakarathne,

Suzuki,

Mäll

et al. 2024

Preprint

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In the context of ongoing discussions about climate change, the focus on beach resilience has gained significant attention in contemporary studies. However, a comprehensive understanding of beach resilience, particularly in the short term, remains limited. This study utilizes a dataset of 104 storm events in Hasaki beach, located on the East coast of Japan, to investigate the 10-day beach recovery. The study considers four types of distinct beach profile patterns based on sandbar formations. Employing XGBoost and the SHAP explanation method, the influence of morphometric indicators on beach resilience were explored. Resilient beach profiles are anticipated to exhibit rapid recovery following erosional storm events. The results reveal that morphometrics play a crucial role in determining the short-term, 10-day, recovery of beaches, with specific morphometric features demonstrating pronounced effects based on profile patterns. The study contributes to the current knowledge of post-storm beach recovery and provides insights that could inform discussions on beach resilience.

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On improved nearshore bathymetry estimates from satellites using ensemble and machine learning approaches

Cited by 15 publications

References 62 publications

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Beach resilience to storm-induced erosion: a machine learning-driven approach to identify key morphometrics to recovery

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