Inference of Water Quality Index Using ANFIA and PCA

Sahoo, Mrunmayee Manjari; Patra, Kanhu Charan; Khatua, Kishanjit Kumar

doi:10.1016/j.aqpro.2015.02.139

Cited by 48 publications

(18 citation statements)

References 4 publications

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“…Their idea was to reduce the number of variables to be introduced in the index and to change the aggregation method, while keeping its accuracy. Next, they used a random database to test their index and showed it gives similar results to NSFWQI and WEPWQI (see Appendix D. 2…”

Section: For Calculation Details)mentioning

confidence: 98%

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Water Quality Indices: Challenges and Application Limits in the Literature

Kachroud

Trolard

Kefi

et al. 2019

Water

220

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Since Horton in 1965, many authors have sought to aggregate different variables characterizing the state of water into a single value called Water Quality Index ( W Q I ). This index is intended to facilitate the operational management of water resources and their allocation for different uses. Detailed and operational description of the main W Q I calculations are here reviewed. The review contains: (1) an historical analysis of the evolution of W Q I calculation methods by looking both at the choice of variables, the methods of weighting and aggregating these variables into a final single value; (2) an illustration of the contradictions observed in the final result when, on the same database, the W Q I is calculated by different methods; (3) the significant progress possible via fuzzy logic to define a W Q I adapted to specific water use.

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Section: For Calculation Details)mentioning

confidence: 98%

“…The increasing population, the expansion of economic activities, and urban sprawl are leading to increased demand for water. The overuse of surface water and groundwater is jeopardizing numerous resources because of the reduction of the available quantities and the deterioration of their quality [1,2].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Indices: Challenges and Application Limits in the Literature

Kachroud

Trolard

Kefi

et al. 2019

Water

220

View full text Add to dashboard Cite

show abstract

“…For instance, Solgi, Pourhaghi, Bahmani and Zarei [21] concluded that wavelet and PCA combinations with support vector regression model can enhance the R 2 of the model during the testing period about 10% when the model is applied for BOD forecasting. Similarly, Sahoo, et al [32] applied the PCA-ANFIS model for forecasting of water quality index River Brahmani, India. The results indicated the efficiency of the proposed model for modeling the index.…”

Section: Pca-wavelet-ann and -Anfis Modelsmentioning

confidence: 99%

Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis

et al. 2020

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In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers.

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“…For instance, Solgi et al (2017) concluded that wavelet and PCA combinations with support vector regression model can enhance the R 2 of the model during the testing period about 10% when the model is applied for BOD forecasting. Similarly, Sahoo, Patra, and Khatua (2015) applied the PCA-ANFIS model for forecasting of water quality index River Brahmani, India. The results indicated the efficiency of the proposed model for modeling the index.…”

Section: Wanfis-pc4mentioning

confidence: 99%

Dimension Reduction of Machine Learning-based Forecasting Models Employing Principal Component Analysis

Meng¹,

Qasem²,

Shokri³

et al. 2020

Preprint

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In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-Wavelet-ANN models are compared with those obtained from Wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid Wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the Wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-Wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicators in rivers.

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Inference of Water Quality Index Using ANFIA and PCA

Cited by 48 publications

References 4 publications

Water Quality Indices: Challenges and Application Limits in the Literature

Water Quality Indices: Challenges and Application Limits in the Literature

Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis

Dimension Reduction of Machine Learning-based Forecasting Models Employing Principal Component Analysis

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