Applications of Computational and Statistical Models for Optimizing the Electrochemical Removal of Cephalexin Antibiotic from Water

Arab, Maliheh; Faramarz, Mahdieh Ghiyaasi; Hashim, Khalid

doi:10.3390/w14030344

Cited by 28 publications

(10 citation statements)

References 38 publications

(67 reference statements)

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“…The use of these artificial intelligence-based techniques has gained vogue among hydrologists in recent years [10][11][12]. ANN models have been successful in processes such as estimating river flows (flow, level, flow volume), making flood warnings, operating reservoirs for flood control, determining the water potential of the stream, hydroelectric production in dry periods, and planning transportation in streams [13,14]. Moreover, ANNs have been used successfully in hydrology.…”

Section: Introductionmentioning

confidence: 99%

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Kilinç¹

2022

Water

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Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the environment. Modelling and estimating river flow in the hydrological process is crucial in terms of effective planning, management, and sustainable use of water resources. Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. For this purpose, three hydrological stations were utilized in the study along the Orontes River basin, Karasu, Demirköprü, and Samandağ, respectively. The timespan of Demirköprü and Karasu stations in the study was between 2010 and 2019. Samandağ station data were from 2009–2018. The datasets consisted of daily flow values. In order to validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the three FMSs. Statistical methods such as linear regression and the more classical model autoregressive integrated moving average (ARIMA) were used during the comparison process to assess the proposed method’s performance and demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression models.

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Section: Introductionmentioning

confidence: 99%

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Kilinç¹

2022

Water

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“…The EC method depends on the destabilisation of the pollutants through liberating coagulation ions from sacrificial electrodes under the effects of electrical current, usually direct current (Fekete et al 2016;Moussa et al 2017;Mena et al 2019). The minimal production of sludge, cost-effectiveness, compacted installation, and automobility are the key advantages of the EC method that attracted both researchers and industry (Hakizimana et al 2017;Lu et al 2021;Arab et al 2022). Additionally, external chemical additions are not required to perform the EC reactors, which is a huge advantage in favour of the environment (Das & Nandi 2020;Shahedi et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Electrochemical defluorination of water: an experimental and morphological study

Abdulredha

Al-Samarrai

Hussein

et al. 2022

Journal of Water, Sanitation and Hygiene for Development

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This experimental study concerns the elimination of fluoride from water using an electrocoagulation reactor having a variable flow direction in favour of increasing the electrolysing time, saving the reactor area, and water mixing. The detention time of the space-saver EC reactor (S-SECR) was measured and compared to the traditional reactors using an inert dye (red drain dye). Then, the influence of electrical current (1.5 ≤ δ ≤ 3.5 mA cm−2), pH of water (4 ≤ pH ≤ 10), and distance between electrodes (5 ≤ ϕ ≤ 15) on the defluoridation of water was analysed. The effect of the electrolysing activity on the electrodes' morphology was studied using scanning electron microscopy (SEM). Additionally, the operational cost was calculated. The results confirmed the removal of fluoride using S-SECR met the guideline of the World Health Organization (WHO) for fluoride levels in drinking water of ≤1.5 mg/L. S-SECR abated fluoride concentration from 20 mg/L to the WHO's guideline at δ, ϕ, pH, operational cost, and power consumption of 2.5 mA cm−2, 5 mm, 7, 0.346 USD m−3, and 5.03 kWh m−3, respectively. It was also found the S-SECR enhanced the detention time by 190% compared to the traditional reactors. The appearance of dents and irregularities on the surface of anodes in the SEM images proves the electrolysing process.

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“…In the above literature, the hybrid model can well improve the prediction performance of the data in the experiments, so it is necessary to select a suitable optimization method for determining the optimal parameters of the model and optimizing the model structure when determining the hybrid model. In recent years, neural network models have been successfully applied to the fields of flood control, sedimentation, and water quality, achieving good results [26][27][28]. However, there have been fewer studies related to the application of artificial neural network models to alpine glacier basins with complex hydrological mechanisms, such as Ji in the alpine glacier basin of the Kumaric and Toxkan rivers [29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Yang

Maihemuti

Simayi

et al. 2022

Water

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The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins.

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Applications of Computational and Statistical Models for Optimizing the Electrochemical Removal of Cephalexin Antibiotic from Water

Cited by 28 publications

References 38 publications

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Electrochemical defluorination of water: an experimental and morphological study

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

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