Bayesian structural equation modeling for coastal management: The case of the Saemangeum coast of Korea for water quality improvements

Kim, Jinah; Park, Jinah

doi:10.1016/j.ocecoaman.2016.10.014

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…Importantly, this method has the potential to integrate climate and risk data, enabling more accurate predictions in the future. Bayesian [168] is a probability-based method that combines prior information about unknown parameters with sample information, using the Bayesian formula to obtain posterior information. Based on the posterior information, unknown parameters can be inferred, yielding good results in small samples.…”

Section: Prediction Of Water Quality Through Coupling Hydrodynamics A...mentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

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Section: Prediction Of Water Quality Through Coupling Hydrodynamics A...mentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Numerical modeling was found to be an important tool in the planning, management, and decision-making to reduce wastewater discharge (Doan, Nguyen, Quach, Tran, & C.D.Nguyen, 2019). In data-driven modeling, the most pressing issue is the creation of a probabilistic model with logical arguments and its accuracy to represent a phenomenon (Kim & Park, 2017).…”

Section: Water Quality Monitoringmentioning

confidence: 99%

Spatio-Temporal Water Quality Mapping Using Satellite Data Around a Mangrove Plantation in Cagsao, Calabanga, Camarines Sur

Saddi

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The Cagsao mangrove is a thriving young forest along the San Miguel Bay (SMB), Camarines Sur. To establish the Spatio-temporal Water Quality mapping, data from the Chesapeake Bay, an estuary in the United States of America (USA), was sourced as the train set for this study. Spatio-temporal maps of chlorophyll and dissolved oxygen were generated using Linear Regression (LR) models which were derived from the train set and satellite images of the SMB. GNU (GNU’s not UNIX) Octave was used for the image processing, computing, and analysis. There were three phases in the image processing conducted in this study, 1) extraction of image data of the corresponding measure points from the train area, 2) conversion of the satellite study area to a two-color raster image, and 3) generation of the spatio-temporal maps from the analysis. The study found that the SMB is in the range of Mesotrophic to Moderate Eutrophic classification. The decay from two other point sources (Manga River and Libmanan River) was compared to that of Tigman River, an adjacent river to the Cagsao mangrove forest to determine variations and impact of the mangrove forest in the water quality of the SMB. The presence of Cagsao mangrove forest was found to affect the gap of increasing chlorophyll levels from shore toward the bay center in the adjacent Tigman River unlike Manga River and Libmanan River, which have both no adjacent mangrove forest in the river mouth area. The corresponding satellite images for the dataset taken during and near the date of the train area measurements were also extracted.

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“…These studies not only prepare theoretical results but also try to produce important practical values. The BSEM technique, called Bayesian approach and based on the Bayesian approach, has been applied to substantive real research in various disciplines, including a much wider class of models, such as marketing research, diabetes research, medicine, psychology, coastal management and water quality, job satisfaction and life satisfaction [3,[6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The Comparison of Classical and Bayesian Structural Equation Models Through Ordered Categorical Data: A Case Study of Banking Service Quality

ERKAN

Doğan

Tatlidil

2023

Gazi University Journal of Science

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This study aims to compare classical SEM and BSEM in terms of ordered categorical data. In order to show the relationship between service dimensions and banks’ customers’ satisfactions, the data were analysed with classical SEM and BSEM parameter estimation method. In the Banking Service Model (Servqual), which consists of sequential categorical data, classical SEM and BSEM parameter estimation methods were compared to evaluate customer satisfaction. LISREL and OpenBUGS package programs were used for the application. In classical SEM, parameter estimations were made according to Maximum Likelihood (ML) estimation method. In the BSEM analysis, the threshold value approach was used for prior knowledge because of the ordered categorical structure of the data. As a result, service dimensions affecting satisfaction according to ML parameter estimation method were found: Assurance, Physical Appearance, and Accessibility. In addition to these, Reliability as a service dimension was found to be also statistically significant in BSEM.

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Bayesian structural equation modeling for coastal management: The case of the Saemangeum coast of Korea for water quality improvements

Cited by 6 publications

References 39 publications

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Spatio-Temporal Water Quality Mapping Using Satellite Data Around a Mangrove Plantation in Cagsao, Calabanga, Camarines Sur

The Comparison of Classical and Bayesian Structural Equation Models Through Ordered Categorical Data: A Case Study of Banking Service Quality

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