Artificial neural network and mathematical approach for estimation of surface water quality parameters (case study: California, USA)

Salami, E. S.; Salari, Marjan; Rastegar, Mohamad; Sheibani, Solmaz Nikbakht; Ehteshami, Majid

doi:10.5004/dwt.2021.26709

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…The results showed that 0.007 g of regenerated silver graphene oxide nanocomposite @ at pH = 5 after 3600 s contact time could remove 88% (188.57 mg/g) of sulfamethoxazole from 0.05 dm3 solution (initial concentration 30 mg/dm3) [59] pH (2)(3)(4)(5)(6)(7)(8)(9); temperature ( °C) (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40); antibiotic volume (mL) (1-5)…”

Section: Discussionmentioning

confidence: 99%

“…Azari et al evaluated the ultrasonic dispersed solid-phase magnetic adsorption method with a high-performance liquid chromatographic system to remove diethyl phthalate (DEP). The primary interaction of parameters such as pH (3)(4)(5)(6)(7)(8)(9)(10)(11), adsorbent dose (0.10-0.50 g L −1 ), ultrasound time (1-5 min), and DEP concentration (5-10 mg/L) was considered. The results showed that MGO has outstanding potential as an adsorbent to remove phthalates from contaminated water [38].…”

Section: Optimum Levels Of Independent Variables and Response Valuesmentioning

confidence: 99%

“…Water is a natural and essential resource for human survival, and that is why human beings have always been looking for clean and healthy water for life. Various methods were carried out to evaluate the quality of surface water and groundwater [1][2][3][4][5][6]. Today, freshwater resources are endangered due to increased population growth and climate change [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Optimization by Box–Behnken Design and Synthesis of Magnetite Nanoparticles for Removal of the Antibiotic from an Aqueous Phase

Salari

2022

Adsorption Science & Technology

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Some environmental problems caused by the intrusion of active drug ingredients, especially antibiotics, into water resources pose a serious threat. Ciprofloxacin (CIP) is an antibiotic from the group of fluoroquinolones that is used extensively in the treatment of bacterial infections. The presence of drug residues in the environment, especially in water resources, is an essential issue due to their stability and nondegradability. This study is aimed at investigating the efficiency of magnetite (Fe3O4) nanoparticles and the effect of independent variables, including initial concentrations of CIP (35-80 mg/L), adsorbent doses (20–60 mg), and pH values (4–10) at reaction time (80 min) for the removal efficiency of CIP antibiotics based on the Box-Behnken design (BBD) method. The analysis of variance (ANOVA) results indicated that a quadratic model was convenient for modeling CIP removal. The first step, the coprecipitation method, was appropriate for the preparation of Fe3O4 nanoparticles and developed as highly efficient adsorbents. Synthesized nanoparticles were later characterized by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Fourier transform infrared spectra (FT-IR). The results of XRD have shown that angles for the peaks at 2 θ = 30 , 35.22 , 43.35 , 53.68 , 57 , 62.79 , and 71.38 deg , which corresponded to the crystal planes 220, 311, 400, 422, 511, 440, and 535, respectively, were consistent with standard peaks of magnetite and a cubic face structure. The obtained results indicated that the CIP removal efficiency was 74.44% under optimum operation parameters: initial concentration of CIP 44.15 (mg/L), adsorbent dosage of 59.6 (mg), pH ≅ 5 , and contact time of 80 min. In fact, a cooperative agreement between model prediction and experimental data using BBD with significant R 2 values of 0.95 was observed. Based on the results, magnetite nanoparticles have an excellent ability to remove antibiotics from an aqueous phase.

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Section: Discussionmentioning

confidence: 99%

Section: Optimum Levels Of Independent Variables and Response Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization by Box–Behnken Design and Synthesis of Magnetite Nanoparticles for Removal of the Antibiotic from an Aqueous Phase

Salari

2022

Adsorption Science & Technology

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“…These environmental parameters, which are used as the ANN's input, may differ among modeling studies of coastal eutrophication. Salami et al [23] created a back-propagation ANN for predicting coastal Chl-a values near Grant Line Canal, California, USA, based on electric conductivity (EC), water temperature (WT), and pH parameters. Even though only three monitoring parameters were used as the model's input, the model calculated the Chl-a variable with a satisfactory accuracy rate (75.9%).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

Hadjisolomou,

Rousou,

Antoniadis

et al. 2023

Water

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Eutrophication is a major environmental issue with many negative consequences, such as hypoxia and harmful cyanotoxin production. Monitoring coastal eutrophication is crucial, especially for island countries like the Republic of Cyprus, which are economically dependent on the tourist sector. Additionally, the open-sea aquaculture industry in Cyprus has been exhibiting an increase in recent decades and environmental monitoring to identify possible signs of eutrophication is mandatory according to the legislation. Therefore, in this modeling study, two different types of artificial neural networks (ANNs) are developed based on in situ data collected from stations located in the coastal waters of Cyprus. These ANNs aim to model the eutrophication phenomenon based on two different data-driven modeling procedures. Firstly, the self-organizing map (SOM) ANN examines several water quality parameters’ (specifically water temperature, salinity, nitrogen species, ortho-phosphates, dissolved oxygen, and electrical conductivity) interactions with the Chlorophyll-a (Chl-a) parameter. The SOM model enables us to visualize the monitored parameters’ relationships and to comprehend complex biological mechanisms related to Chl-a production. A second feed-forward ANN model is also developed for predicting the Chl-a levels. The feed-forward ANN managed to predict the Chl-a levels with great accuracy (MAE = 0.0124; R = 0.97). The sensitivity analysis results revealed that salinity and water temperature are the most influential parameters on Chl-a production. Moreover, the sensitivity analysis results of the feed-forward ANN captured the winter upwelling phenomenon that is observed in Cypriot coastal waters. Regarding the SOM results, the clustering verified the oligotrophic nature of Cypriot coastal waters and the good water quality status (only 1.4% of the data samples were classified as not good). The created ANNs allowed us to comprehend the mechanisms related to eutrophication regarding the coastal waters of Cyprus and can act as useful management tools regarding eutrophication control.

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Estimation of Hourly Salinity Concentrations Using an Artificial Neural Network

Alarcon

Linhoss

Kelble

et al. 2021

Computational Science and Its Applications – ICCSA 2021

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Artificial neural network and mathematical approach for estimation of surface water quality parameters (case study: California, USA)

Cited by 5 publications

References 30 publications

Optimization by Box–Behnken Design and Synthesis of Magnetite Nanoparticles for Removal of the Antibiotic from an Aqueous Phase

Optimization by Box–Behnken Design and Synthesis of Magnetite Nanoparticles for Removal of the Antibiotic from an Aqueous Phase

Data-Driven Models for Evaluating Coastal Eutrophication: A Case Study for Cyprus

Estimation of Hourly Salinity Concentrations Using an Artificial Neural Network

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