Neuro-fuzzy ensemble techniques for the prediction of turbidity in water treatment plant

Abba, S.I.; Abdulkadir, Rabiu Aliyu; Gaya, Muhammad Sani; Saleh, M.A.; Esmaili, Parveneh; Jibril, M. B.

doi:10.1109/nigeriacomputconf45974.2019.8949629

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…Being the fundamental modelling methods, the traditional and physic-based methods explain the physical processes but still attributed with various weakness such as, time consuming, computational burden, failure to capture chaotic, and complex process. In contrast, it was known to overcome the above limitation but neglect the physical process, particularly when focus is on the accuracy and reliability of the estimation rather than understanding the simple physical process [54]- [57]. AI and traditional model were employed in our study.…”

Section: Introductionmentioning

confidence: 99%

Interpretation the Influence of Hydrometeorological Variables on Soil Temperature Prediction Using the Potential of Deep Learning Model

Elsayed¹,

Gupta

Chaudhary

et al. 2023

Kbes

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The importance of soil temperature (ST) quantification can contribute to diverse ecological modelling processes as well as for agricultural activities. Over the literature, it was evident that soil supports more than 95% of living habitats and food production on earth, and this demand will increase to 500 years’ times in expected consumption in 2060. This paper aims to analyses the contrastive approach to predict the ST of a certain region with the help of different machine learning models, including Random Forest (RF), Support Vector, Neural Network (NN), Linear Regression (LR) and Long Short-Term Memory Network (LSTM). The study was utilized the hourly humidity, dew point, rainfall, solar radiation, and barometer readings for the formulation of the models. Various performance criteria were employed to evaluate the prediction skills of the models and the results depicted that the promising ability belong to LSTM despite the acceptable prediction accuracy achieved by other models. The modelling outcomes revealed that LSTM model attained the lowest root mean square error (RMSE = 3.3255) decreased the average prediction error by 6% with regards to NN (RMSE = 3.4796), SVM (RMSE = 3.5766), and RF (RMSE = 3.8128), and improved the prediction accuracy of LR by 15%. The model is in compliance with the latest machine learning industry standards and allows low-cost experimental performances on low powered edge computing devices.

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Section: Introductionmentioning

confidence: 99%

Interpretation the Influence of Hydrometeorological Variables on Soil Temperature Prediction Using the Potential of Deep Learning Model

Elsayed¹,

Gupta

Chaudhary

et al. 2023

Kbes

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“…Therefore, many AI techniques have been applied to turbidity prediction (Khairi et al 2016;Baghalian & Ghodsian 2017;Daghbandan et al 2019;Song & Zhang 2020;Zounemat-Kermani et al 2020). Abba et al (2019) proposed a neuro fuzzy ensemble technique to predict turbidity in water treatment plants. Nieto et al (2014Nieto et al ( , 2020 proposed a new practical model for long-term prediction of turbidity based on support vector machine and particle swarm optimization.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

Wang

Chang

et al. 2021

Water Supply

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During the drinking water treatment, the uncertain changes of raw water quality bring great difficulties to the control of flocculants dosage, especially because the feedback information based on the effluent turbidimeter of sedimentation tank can only be obtained after a long time when the influent water quality changes for the large lag characteristics of flocculation process. The prediction of effluent turbidity of sedimentation tank can effectively solve aforementioned problem. Given that it is difficult for the ordinary random forest (RF) model to accurately predict the effluent turbidity of sedimentation tank for the complicated changes of raw water quality, an improved random forest (IRF) model composed of long-term and short-term parts is proposed, which can capture the periodicity and time-varying characteristics of influent water quality data. The experimental results show that, the root mean square error and mean absolute percentage error of IRF model in Baiyangwan waterworks are improved 67.52% and 67.91% respectively, compared with those of ordinary RF model. The proposed effluent turbidity predictions are also successfully developed in Xujiang waterworks and Xiangcheng waterworks of Suzhou, China. This research provides an effective method for real-time predicting the effluent turbidity of sedimentation tank according to the influent water quality data.

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“…Coagulation is a complicated nonlinear process because there are many physical and chemical variables that influence this process (Kim, C. M., & Parnichkun, 2017) Modeling a complex process such as in water treatment plants is not easy, due to the non-linear processes (physical, chemical, biological, and biochemical) (S.I. Abba et al , 2019) Modeling is usually done in one of the following ways: Techniques based on data have gotten a lot of attention recently. Due to their ease of use in the field of process monitoring implementation, and fewer underlying requirements.…”

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confidence: 99%

Predicting turbidity and Aluminum in drinking water treatment plants using Hybrid Network (GA- ANN) and GEP

Alsaeed

Alaji

Ebrahim

2021

Preprint

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Abstract. Turbidity is the most important parameter needed to check the status of drinking water, as it is an integrated parameter because its high values indicate high values of other parameters related to water quality. Coagulation and flocculation are the most essential processes for the removal of turbidity in drinking water treatment plants. Using alum coagulants increases the aluminum residuals in treated water, which have been linked to Alzheimer's disease pathogenesis.In this paper, a hybrid algorithm (GA-ANN) used to predict the turbidity values in the drinking water purification plant in Al Qusayr was used.The models were constructed using raw water data: turbidity of raw water, pH, conductivity, temperature, and coagulant dose, to predict the turbidity values coming out of the plant.Several models built and fitness detected for each model, the network with the highest fitness was selected, and then a hybrid prediction network was constructed.The selected network was the most able to predict turbidity of the outlet with high accuracy with a correlation coefficient (0. 9940) and a root mean square error of 0.1078.And 4 equations for determining the value of the residual aluminum was obtained using Gene expression method, and the best equation produced results with very good accuracy, in this regard it can be referred to RMSE = 0.02 R = 0.9 for the best model.

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Neuro-fuzzy ensemble techniques for the prediction of turbidity in water treatment plant

Cited by 10 publications

References 17 publications

Interpretation the Influence of Hydrometeorological Variables on Soil Temperature Prediction Using the Potential of Deep Learning Model

Interpretation the Influence of Hydrometeorological Variables on Soil Temperature Prediction Using the Potential of Deep Learning Model

Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model

Predicting turbidity and Aluminum in drinking water treatment plants using Hybrid Network (GA- ANN) and GEP

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