Modeling of Water Quality Parameters in Manzala Lake Using Adaptive Neuro-Fuzzy Inference System and Stochastic Models

The detection of waterborne bacteria is crucial to prevent health risks. Current research uses soft computing techniques based on Artificial Neural Networks (ANN) for the detection of bacterial pollution in water. The limitation of only relying on sensor-based water quality analysis for detection can be prone to human errors. Hence, there is a need to automate the process of real-time bacterial monitoring for minimizing the error, as mentioned above. To address this issue, we implement an automated process of water-borne bacterial detection using a hybrid technique called Adaptive Neuro-fuzzy Inference System (ANFIS), that integrates the advantage of learning in an ANN and a set of fuzzy if-then rules with appropriate membership functions. The experimental data as the input to the ANFIS model is obtained from the open-sourced dataset of government of India data platform, having 1992 experimental laboratory results from the years 2003-2014. We have included the following water quality parameters: Temperature, Dissolved Oxygen (DO), pH, Electrical conductivity, Biochemical oxygen demand (BOD) as the significant factors in the detection and existence of bacteria. The membership function changes automatically with every iteration during training of the system. The goal of the study is to compare the results obtained from the three membership functions of ANFIS- Triangle, Trapezoidal, and Bell-shaped with 35 = 243 fuzzy set rules. The results show that ANFIS with generalized bell-shaped membership function is best with its average error 0.00619 at epoch 100.

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Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization

Sustainability ◽

10.3390/su13084576 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4576

Author(s):

Muhammad Izhar Shah ◽

Taher Abunama ◽

Muhammad Faisal Javed ◽

Faizal Bux ◽

Ali Aldrees ◽

...

Keyword(s):

Water Quality ◽

Surface Water ◽

Predictive Models ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Surface Water Quality ◽

Inference System ◽

Input Optimization ◽

Neuro Fuzzy ◽

Soft Computing Techniques

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.

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Prediction of Water Quality Index Using Neuro Fuzzy Inference System

Water Quality Exposure and Health ◽

10.1007/s12403-011-0054-7 ◽

2011 ◽

Vol 3 (3-4) ◽

pp. 175-191 ◽

Cited By ~ 25

Author(s):

Mrutyunjaya Sahu ◽

S. S. Mahapatra ◽

H. B. Sahu ◽

R. K. Patel

Keyword(s):

Water Quality ◽

Water Quality Index ◽

Fuzzy Inference System ◽

Quality Index ◽

Fuzzy Inference ◽

Inference System ◽

Neuro Fuzzy

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Water Quality Control for Shrimp Pond Using Adaptive Neuro Fuzzy Inference System : The First Project

Journal of Physics Conference Series ◽

10.1088/1742-6596/953/1/012134 ◽

2018 ◽

Vol 953 ◽

pp. 012134

Author(s):

F Umam ◽

H Budiarto

Keyword(s):

Water Quality ◽

Quality Control ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Water Quality Control ◽

Inference System ◽

Shrimp Pond ◽

Neuro Fuzzy

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Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study

Applied Water Science ◽

10.1007/s13201-019-1044-3 ◽

2019 ◽

Vol 9 (7) ◽

Cited By ~ 4

Author(s):

Salim Heddam ◽

Hadi Sanikhani ◽

Ozgur Kisi

Keyword(s):

Artificial Intelligence ◽

Water Quality ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Test Phase ◽

Pigment Concentration ◽

Quality Data ◽

Subtractive Clustering ◽

Inference System ◽

Neuro Fuzzy

Abstract In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 μg/L and 0.148 μg/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 μg/L and 0.031 μg/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 μg/L and 0.200 μg/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors.

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