A Type-2 Fuzzy Logic Approach for Forecasting of Effluent Quality Parameters of Wastewater Treatment

Bhattacharjee, Samyabrata; Alattas, Khalid A.; El-Sousy, Fayez F. M.; Mohammadzadeh, Ardashir; Asad, Jihad; Mobayen, Saleh; Alshaikh, Noorhan

doi:10.1155/2022/1965157

Cited by 3 publications

(3 citation statements)

References 29 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e number of particles chosen was 15. e equations (from equation nos. [10][11][12][13][14][15] for model parameters are shown in Table 6.…”

Section: Voting Classifier Optimized Using Particle Swarm Optimizatio...mentioning

confidence: 99%

“…e considered quality parameters are level of chlorophyll, turbidity conductance, and level of dissolution. A time-series data-based water quality prediction system for public water utility data is presented in [12]. Different machine learning algorithms like SVM, logistic regression (LR), and deep neural network (DNN) have been used for prediction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality

Agrawal¹,

Jain

Khatri³

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Over the last few years, different contaminants have posed a danger to the quality of the water. Hence modelling and forecasting water quality are very important in the management of water contamination. The paper proposes an ensemble machine learning-based model for assessing water quality. The results of the proposed model are compared with several machine learning models, including k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree. The considered dataset contains seven statistically important parameters: pH, conductivity, dissolved oxygen, Biochemical Oxygen Demand, nitrate, total coliform, and fecal coliform. The water quality index is calculated for assessing water quality. To utilize an ensemble approach, a voting classifier has been designed with hard voting. The highest prediction accuracy of 99.5% of the water quality index is presented by the voting classifier as compared to the prediction accuracy of 99.2%, 90%, 79%, and 99% presented through k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree, respectively. This was further enhanced to 99.74% using particle swarm based optimization.

show abstract

“…e number of particles chosen was 15. e equations (from equation nos. [10][11][12][13][14][15] for model parameters are shown in Table 6.…”

Section: Voting Classifier Optimized Using Particle Swarm Optimizatio...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel PSO Optimized Voting Classifier Approach for Predicting Water Quality

Agrawal¹,

Jain

Khatri³

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In this study, a T2FS is developed for attaining corporate sustainability. An IT2FLC is an extensive version of T1-FLS, and it is more accurate and sensitive as reported in literature [26,27]. Comparative reviews are tabulated in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Financial Performance Assessment by a Type‐2 Fuzzy Logic Approach

Wang

Bhattacharjee

Kausar

et al. 2023

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Any company must constantly innovate if they want to maintain its market share in the present cutthroat and unstable industry. Innovation has a big influence on consumer behavior, yet it goes against the principles of sustainability. The issue of sustainability has become crucial to their company’s growth. In order to evaluate a business firm’s sustainability performance statistically, a new and effective fuzzy logic tool is created. Evolution and assessment are performed by a novel interval type-2 fuzzy logic inference system. The judgment of the inference system is carried out on the basis of type-2 fuzzy logic (T2FL), principal component analysis (PCA), and statistical data analysis. The main input variables include corporate environmental performance (CEP) and corporate financial performance (CFP). The suggested approach can effectively examine a corporation’s sustainable performance, according to experimental findings. A unique approach that makes use of language variables and if-then logic to assist quantitative business sustainability events is the link between CEP and CFP. The recommended test will provide senior administrative leaders with useful information to supervise natural concerns correctly and gauge their commitment to company success.

show abstract