Wastewater discharge quality prediction using stratified sampling and wavelet de-noising ANFIS model

Fu, Zhao; Cheng, Jiao; Yang, Mei; Batista, Jacimaria R.; Jiang, Yingtao

doi:10.1016/j.compeleceng.2020.106701

Cited by 28 publications

(9 citation statements)

References 18 publications

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“…5). Prediction excluding COD and TS results in increased RMSE which shows the importance of the same.In case of DO, phosphate and nitrate concentrations obtains the highest error ratio of 0.98 and 0.93(Table4.6).…”

mentioning

confidence: 90%

“…These two types of solids can be identified by using a glass fiber filter that the water sample passes through [16]. The suspended solids are retained on the top of the filter and the dissolved solids are that pass through the filter with the water [5].…”

Section: Study Areamentioning

confidence: 99%

“…Bicarbonates sulphates, chlorides, and nitrates are some of the forms they take. [5]. Hardness is confirmed when value is more than 300 mg/L and also water is said to be soft if value is less than 75 mg/L.…”

Section: Study Areamentioning

confidence: 99%

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Water Quality Modelling of River Periyar Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

Shine¹,

Madhu

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Water is requisite on earth for life to survive. The study area of the present work is the river Periyar, the longest river in Kerala, India. The overall objective of the present work is to compare the accuracy and performance of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the training and prediction of dissolved oxygen (DO) concentration and biochemical oxygen demand (BOD) in the river. The models were used to examine secondary data from the past for four water quality metrics generated at five monitoring stations located along the river Periyar to predict DO concentrations and BOD. The performance of the models was examined using root mean square error (RMSE) and coefficient of correlation (R) values. This study revealed that ANN is superior to ANFIS in most scenarios. In most situations, ANFIS appears to get the superiority in training, however, ANN seemed to have the dominant position in testing and validation. The inputs having the highest relevance in the case of DO prediction were identified to be nitrate and phosphate concentrations, whereas total solids and COD were seen to be the most impactful in the case of BOD forecasting employing sensitivity analysis.

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confidence: 90%

Section: Study Areamentioning

confidence: 99%

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Water Quality Modelling of River Periyar Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

Shine¹,

Madhu

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Various types of matrix and composite indexes replacing several parameters are created to simplify the procedures of wastewater quality assessment [23][24][25][26][27]. Nowadays, in order to limit measurement campaigns and to facilitate the analysis and evaluation of measurement data, statistical optimization and forecasting methods, also with the use of artificial intelligence, are increasingly used [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of wastewater quality in Lodz sewage system (Poland)—do the current solutions enable the protection of WWTP and receiving water?

Sakson

Brzezińska

Bandzierz

et al. 2021

Int J Energy Environ Eng

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Solving urban wastewater management problems requires knowledge of wastewater composition and variability. In the case of combined sewerage, this applies to both dry and wet weather. Wastewater composition is changing as a result of the appearance of new substances on the market, the changes in inhabitant lifestyle and the catchment characteristic; therefore, it must be constantly monitored. At the same time, due to the time-consuming and high costs of measurement campaigns, solutions that could limit their scope and facilitate the interpretation of the results are sought. This paper presents the results of the measurement campaign conducted in 2018–2021. The aim of the monitoring was, inter alia, assessment of wastewater composition in terms of threats to wastewater treatment plant and urban rivers, which are receivers of discharge from combined sewer overflows. The obtained results were analyzed using the multivariate statistical methods: Principal Component Analysis and Cluster Analysis. However, the applied methods did not allow for the full identification of the relationship between the wastewater quality parameters as well as the differences and similarities in the wastewater composition from individual parts of the city, which could simplify and reduce the measurement campaigns in the future. Therefore, in the case of large urban catchments, it is necessary to introduce other solutions to control the wastewater composition.

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“…Some studies have shown that the ANFIS method is extremely accurate in some natural sciences, such as groundwater prediction (Elzain et al 2021;Seifi et al 2020), soil (Mehdizadeh et al 2020), 318 predicting erosion (Islam et al 201;Kaboodvandpour et al 2015), and water quality (Fu et al 2020), and can provide better results than ANN and fuzzy models.…”

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confidence: 99%

Predicting Fault Locations based on Morphometric Features of Alluvial Fans and Basins using Artificial Neural Networks

Mokarram

Pourghasemi

Tiefenbacher³

2021

Preprint

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The aim of this study is to investigate the morphometry of alluvial fans located in the vicinity of the Sabzevar and Sang-Sefid faults in northeastern Iran to determine their influence on erosion Principal component analysis (PCA) was used to select the most important morphometric factors affecting erosion. The data regarding the important parameters were input into adaptive neural-fuzzy networks (ANFIS) to predict erosion rates. The asymmetric factor (Af), hypsometric integral (Hi), and basin shape (BS) indicate that most of the sub-basins are tectonically active. The results of the PCA revealed that the most important parameters affecting erosion were Af, Pf, Lf, Rf, Vf, Pb, Ab, LC, Lb, Dd, and the geological unit. The ANFIS method showed that among the soil erosion prediction models, the FCM hybrid model had the highest accuracy. It is concluded that morphometric features can be used to predict the erosion processes in the basin.

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Wastewater discharge quality prediction using stratified sampling and wavelet de-noising ANFIS model

Cited by 28 publications

References 18 publications

Water Quality Modelling of River Periyar Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

Water Quality Modelling of River Periyar Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

Monitoring of wastewater quality in Lodz sewage system (Poland)—do the current solutions enable the protection of WWTP and receiving water?

Predicting Fault Locations based on Morphometric Features of Alluvial Fans and Basins using Artificial Neural Networks

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