Edge Detection Technique for Simultaneous Measurement of Total Suspended Solids and Turbidity

Hakiki, Rijal

doi:10.21660/2023.111.4003

Cited by 1 publication

(2 citation statements)

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“…Digital image processing techniques analyze the gray levels in water images to estimate turbidity levels. For instance, studies have demonstrated how image pixels correlate with water turbidity [15][16][17]. These methods, however, also face challenges such as sensitivity to lighting conditions and image quality.…”

Section: Introductionmentioning

confidence: 99%

“…This methodology was adopted to maintain a controlled environment and eliminate variables that could introduce noise and optical aberrations. In this way, we ensure that the optimization algorithms focus on the nature of the database, which consists of black points (suspended solids) on a white background, analogous to liquid samples with suspended solids as referred as referred in articles [15][16][17]27]. The aim of this research is to identify the most suitable optimizer based on the nature of the database and to provide additional information about the performance of each optimizer.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%