Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong

Kumar, Lalit; Afzal, Mohammad Saud; Ahmad, Ashad

doi:10.1016/j.rsma.2022.102260

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…In the literature, ADAM optimizers have reached in turbidity task: 0.89 of AUC (good discrimination between classes) [56], mean square error less than 0.05 [57], R 2 value of 0.80 [58], accuracy of 88.45% [59] and 87% [24]. These values are lower than those that SGD has been able to provide, as seen in the present study.…”

Section: Resultsmentioning

confidence: 43%

Evaluation of Optimization Algorithms for Measurement of Suspended Solids

Lopez-Betancur,

González-Ramírez,

Guerrero-Mendez

et al. 2024

Water

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Advances in convolutional neural networks (CNNs) provide novel and alternative solutions for water quality management. This paper evaluates state-of-the-art optimization strategies available in PyTorch to date using AlexNet, a simple yet powerful CNN model. We assessed twelve optimization algorithms: Adadelta, Adagrad, Adam, AdamW, Adamax, ASGD, LBFGS, NAdam, RAdam, RMSprop, Rprop, and SGD under default conditions. The AlexNet model, pre-trained and coupled with a Multiple Linear Regression (MLR) model, was used to estimate the quantity black pixels (suspended solids) randomly distributed on a white background image, representing total suspended solids in liquid samples. Simulated images were used instead of real samples to maintain a controlled environment and eliminate variables that could introduce noise and optical aberrations, ensuring a more precise evaluation of the optimization algorithms. The performance of the CNN was evaluated using the accuracy, precision, recall, specificity, and F_Score metrics. Meanwhile, MLR was evaluated with the coefficient of determination (R2), mean absolute and mean square errors. The results indicate that the top five optimizers are Adagrad, Rprop, Adamax, SGD, and ASGD, with accuracy rates of 100% for each optimizer, and R2 values of 0.996, 0.959, 0.971, 0.966, and 0.966, respectively. Instead, the three worst performing optimizers were Adam, AdamW, and NAdam with accuracy rates of 22.2%, 11.1% and 11.1%, and R2 values of 0.000, 0.148, and 0.000, respectively. These findings demonstrate the significant impact of optimization algorithms on CNN performance and provide valuable insights for selecting suitable optimizers to water quality assessment, filling existing gaps in the literature. This motivates further research to test the best optimizer models using real data to validate the findings and enhance their practical applicability, explaining how the optimizers can be used with real data.

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Section: Resultsmentioning

confidence: 43%

Evaluation of Optimization Algorithms for Measurement of Suspended Solids

Lopez-Betancur,

González-Ramírez,

Guerrero-Mendez

et al. 2024

Water

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“…Machine learning models have found extensive applications across various fields [82][83][84][85]. For instance, a neural network-based algorithm has been utilized to monitor turbidity in the marine environment [86]. Jun Ma et al [87] employed a combination of Deep Matrix Factorization and Deep Neural Network to accurately predict BOD values.…”

Section: Single Water Quality Prediction Using Machine Learningmentioning

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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“…However, anthropogenic and natural processes can disrupt the physical-chemical balance in a river. This leads to the endangerment of water quality, negatively impacting human health and aquatic biodiversity and causing significant economic implications worldwide [1][2][3][4]. To cite a few, diverse parameters can be used to monitor water quality, such as dissolved oxygen, chlorophyll, and salt concentrations [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Turbidity Modeling in the Mississippi River Using Machine Learning and Sentinel‐2 Remote Sensing Data: A Generalizability Analysis

Costa Rocha,

Oliveira Santos,

Thé

et al. 2024

Preprint

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Turbidity is an important indicator of water quality in hydrology. More traditional ways to monitor turbidity can provide reliable results. However, they are prone to human error, have elevated costs, and lack real-time monitoring capacity. Addressing these hindrances, in this work we combine spectral bands and indices from Sentinel-2 with several machine learning paradigms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations encompassing the Mississippi River, USA. Results show that considering the individual monitoring stations, the ML paradigms for turbidity modeling were satisfactory at locations with a larger range and standard deviation values, achieving a mean R2 value of 59.5%. Tree-based models were the best overall approach, often ranking as the best or second-best performing model. When all the samples from the monitoring stations were used, the XGBoost provided superior output for turbidity modeling, reaching an R2 equal to 75.7%. A comprehensive comparison with the literature found values showed that the models implemented using this study’s methodology could provide competitive results, deeming it as an alternative for turbidity modeling from remote sensing data.

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Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong

Cited by 10 publications

References 39 publications

Evaluation of Optimization Algorithms for Measurement of Suspended Solids

Evaluation of Optimization Algorithms for Measurement of Suspended Solids

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

Enhancing Turbidity Modeling in the Mississippi River Using Machine Learning and Sentinel‐2 Remote Sensing Data: A Generalizability Analysis

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