Modelling of Bunus regional sewage treatment plant using machine learning approaches

Pham, Quoc Bao; Gaya, Muhammad Sani; Abba, S.I.; Abdulkadir, Rabiu Aliyu; Esmaılı, Parvaneh; Linh, Nguyễn Thị Thùy; Sharma, Chetan; Malik, Anurag; Khôi, Đào Nguyên; Dung, Tran Duc; Linh, Do Quang

doi:10.5004/dwt.2020.26160

Cited by 19 publications

(9 citation statements)

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“…57 In practice, while simple average ensemble learning can offer benefits in certain situations, more advanced ensemble methods such as weighted averaging, stacking, and boosting are often used to better harness the diversity and expertise of individual models. 61,62 These methods take into account the strengths and weaknesses of different models, leading to improved performance. 39 It is worth mentioning that the choice of RMSE and NSE for evaluating model performance in brine resource recovery is because both metrics provide a comprehensive view of model accuracy.…”

Section: Acs Appliedmentioning

confidence: 99%

Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions

Abdulazeez,

Abba,

Usman

et al. 2023

ACS Appl. Nano Mater.

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Section: Acs Appliedmentioning

confidence: 99%

Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions

Abdulazeez,

Abba,

Usman

et al. 2023

ACS Appl. Nano Mater.

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“…Various new AI-based models have yet to be applied to COVID-19 situations, despite suggestions in the literature to employ different versions of these models—such as neural networks—for novel COVID-19 modeling. Another reason to investigate novel modeling methods is the fact that correct simulation of COVID-19 in a research region can save money, energy, and time; as a result, the choice of modeling methodology is given a lot of thought when forecasting these important trends [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. On the other hand, studies of COVID-19 related to image segmentation have been explored in [ 12 , 13 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

Alamrouni

Aslanova

Mati

et al. 2022

IJERPH

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Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

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“…The superiority of data-driven models is attributed to certain factors, such as the building of models, type of learning, data type, and basin characteristics. Hence, achieving complex modelling such as that required for HMs requires both black-box and white-box expertise to facilitate the stochastic and experimental process [28][29][30][31]. Despite a number of published technical studies on the simulation of HMs using AI-based models such as artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector machines (SVM), etc.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

et al. 2022

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Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.

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Modelling of Bunus regional sewage treatment plant using machine learning approaches

Cited by 19 publications

References 0 publications

Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions

Recovery of Brine Resources Through Crown-Passivated Graphene, Silicene, and Boron Nitride Nanosheets Based on Machine-Learning Structural Predictions

Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

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