Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Parra, Lorena; Ahmad, Ali; Sendra, Sandra; Lloret, Jaime; Lorenz, Pascal

doi:10.3390/chemosensors12030034

Cited by 20 publications

(3 citation statements)

References 51 publications

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“…Based on this information, the model is then trained to be able to predict outputs based on new input data. As many of the previously discussed methods for sensor placement are iterative approaches, the problem of solving for sensor placement seems to be one to which machine learning is well suited [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Sensor Placement for Modal Testing Using Machine Learning

Kelmar,

Chierichetti,

Davoudi Kakhki

2024

Applied Sciences

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Modal testing is a common step in aerostructure design, serving to validate the predicted natural frequencies and mode shapes obtained through computational methods. The strategic placement of sensors during testing is crucial for accurately measuring the intended natural frequencies. However, conventional methodologies for sensor placement are often time-consuming and involve iterative processes. This study explores the potential of machine learning techniques to enhance sensor selection methodologies. Three machine learning-based approaches are introduced and assessed, and their efficiencies are compared with established techniques. The evaluation of these methodologies is conducted using a numerical model of a beam to simulate real-world scenarios. The results offer insights into the efficacy of machine learning in optimizing sensor placement, presenting an innovative perspective on enhancing the efficiency and precision of modal testing procedures in aerostructure design.

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Section: Introductionmentioning

confidence: 99%

Optimization of Sensor Placement for Modal Testing Using Machine Learning

Kelmar,

Chierichetti,

Davoudi Kakhki

2024

Applied Sciences

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“…Spectrophotometers are developed for water quality monitoring in [12,15,17,23]. References [26,29,30] focus on sensors for measuring water turbidity, while others concentrate on phycocyanin (PC) or chlorophyll-a (Chl-a) monitoring [18], [19], [20], [32], or fluorometers for laboratory use [21]. Fluorometer designs are available for both PC and Chl-a measurements [22].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost LoRa Optical Fluorometer–Nephelometer for Wireless Monitoring of Water Quality Parameters in Real Time

Hagh,

Amngostar,

Chen

et al. 2024

IEEE Sensors J.

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Real-time monitoring of water quality parameters using optical sensors has become widespread. However, the price of commercial sensors and the ability to integrate them into customized sensing networks can limit their application in research and monitoring programs. This study introduces the design, verification, and validation of an innovative, low-cost, portable, lowpower fluorometer-nephelometer device, employing Long-Range Wide Area Network technology. The developed Internet of Things capable device can measure temperature, turbidity, phycocyanin fluorescence (a proxy for cyanobacteria biomass), and chlorophyll-a fluorescence (a proxy for phytoplankton (cyanobacteria plus algae) biomass) in aquatic ecosystems. The fluorometer-nephelometer structure employs one digital thermistor probe, three distinct light-emitting diodes (LEDs) that are amber (590 nm) to excite phycocyanin pigments of cyanobacteria, blue (465 nm) to excite chlorophyll-a pigments of phytoplankton, and near-infrared (870 nm) to measure turbidity through light scattering. Two orthogonal silicon photodiodes are used as detectors set in line with long pass filters of 830 nm for turbidity and 630 nm for phytoplankton. A peristaltic pump circulates water through a polymethylmethacrylate cuvette within a 3D-printed fluorometer assembled in a weatherproof box. The activation by personalization LoRaWAN protocol is utilized for realtime wireless transmission of water quality data while a micro-SD card is employed for storing the data locally. The optical sensor tests are conducted in the laboratory using standard turbidity solutions and pigments. The turbidity, phycocyanin, and chlorophylla sensing ranges are 3-200 FTU, 0.025-2.5 mg-PC/L, and 1-50 µg-chl/L, respectively. In repeated laboratory tests, the relative percent difference is consistently less than 10%.

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“…Convolutional neural networks (CNNs) offer a promising alternative for turbidity measurement. CNNs replicate the human visual cortex, making them highly effective for image analysis tasks such as classification and detection [18,19]. CNNs are mathematical algorithms that replicate how humans learn and mimic the mammalian visual cortex using computational blocks and multiple layers of artificial neurons to approximate any continuous function [20].…”

Section: Introductionmentioning

confidence: 99%

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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Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Cited by 20 publications

References 51 publications

Optimization of Sensor Placement for Modal Testing Using Machine Learning

Optimization of Sensor Placement for Modal Testing Using Machine Learning

A Low-Cost LoRa Optical Fluorometer–Nephelometer for Wireless Monitoring of Water Quality Parameters in Real Time

Evaluation of Optimization Algorithms for Measurement of Suspended Solids

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