Evaluation of Optimization Algorithms for Measurement of Suspended Solids Article Swipe
Daniela López-Betancur
,
Efrén González
,
Carlos Guerrero-Méndez
,
Tonatiuh Saucedo-Anaya
,
Martín Montes Rivera
,
Edith Olmos-Trujillo
,
Salvador Gómez-Jiménez
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/w16131761
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/w16131761
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/w16131761
- https://www.mdpi.com/2073-4441/16/13/1761/pdf?version=1718958988
- OA Status
- gold
- Cited By
- 4
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399892602
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399892602Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/w16131761Digital Object Identifier
- Title
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Evaluation of Optimization Algorithms for Measurement of Suspended SolidsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-21Full publication date if available
- Authors
-
Daniela López-Betancur, Efrén González, Carlos Guerrero-Méndez, Tonatiuh Saucedo-Anaya, Martín Montes Rivera, Edith Olmos-Trujillo, Salvador Gómez-JiménezList of authors in order
- Landing page
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https://doi.org/10.3390/w16131761Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-4441/16/13/1761/pdf?version=1718958988Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-4441/16/13/1761/pdf?version=1718958988Direct OA link when available
- Concepts
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Algorithm, Computer science, Convolutional neural network, Artificial intelligence, Artificial neural network, Mean squared error, Machine learning, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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