scholarly journals Detection of waterborne bacteria using Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 158 ◽  
pp. 05002
Author(s):  
Farhan Mohammad Khan ◽  
Smriti Sridhar ◽  
Rajiv Gupta
Keyword(s):  
Water Quality ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Analysis ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

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.

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

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.

scholarly journals Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

2019 ◽  
Vol 12 (1) ◽  
pp. 45-54 ◽  
Author(s):  
Armin Azad ◽  
Hojat Karami ◽  
Saeed Farzin ◽  
Sayed-Farhad Mousavi ◽  
Ozgur Kisi
Keyword(s):  
Water Quality ◽  
River Water ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals PREDIKSI TINGGI MUKA AIR BENDUNGAN RIAM KANAN MENGGUNAKAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

2018 ◽  
Vol 9 (1) ◽  
pp. 11
Author(s):  
Yusri Ikhwani
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Bendungan riam kanan yang berada kabupaten banjar ialah salah satu waduk terbesar di kalimantan selatan yang ada di aranio, kabupaten banjar. Waduk buatan yang dalam pembangunannya memakan waktu selama 10 tahun ini dibangun membendung 8 sungai yang bersumber dari Pegunungan Meratus. Tujuan utama dibangunnya waduk riam kanan adalah untuk membangun pembangkit listrik tenaga air untuk daerah kalimantan selatan dan sekitarnya.Tujuan penelitian ini ialah untuk memprediksi tinggi muka air bendungan riam kanan menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS) agar dapat bermanfaat dalam kebijakan strategis ketahanan energi khususnya ketahanan pangan dan energi listrik, khususnya ketersediaan air untuk saluran irigasi.Perkiraan prediksi ini menggunakan data tinggi muka air bendungan riam kanan dari tahun 2009 sampai dengan 2015 yang didapatkan dari PLTU riam kanan provinsi kalimantan selatan. Prosedur memprediksi diawali dengan melakukan proses pembagian data, yaitu menjadi data pelatihan dan data pengujian. Setelah itu dilakukan penentuan variabel-variabel pendukung input yang memberikan korelasi cukup signifikan terhadap variabel output. Serelah itu melakukan proses pengujian dengan membandingkan 2 membership function untuk menentukan yang mana memiliki tingkat akurasi yang baik dan nilai error yang rendah dalam memprediksi tinggi muka air bendungan riam kanan.Hasilnya ialah prediksi tinggi muka air bendungan riam kanan menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dengan membandingkan 2 membership function dengan tingkat keakuratan menghasilkan nilai RMSE 0,010065 pada membership function Bell Kata kunci: bendungan riam kanan, anfis, prediksi, tinggi muka air, membership fungtion

scholarly journals Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System

2017 ◽  
Vol 1 (2) ◽  
pp. 65 ◽  
Author(s):  
Gusti Ahmad Fanshuri Alfarisy ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hybrid Learning ◽  
Learning Method ◽  
Rainfall Forecasting ◽  
Inference System ◽  
Previous Event ◽  
Neuro Fuzzy ◽  
High Prediction

Rainfall forcasting is a non-linear forecasting process that varies according to area and strongly influenced by climate change. It is a difficult process due to complexity of rainfall trend in the previous event and the popularity of Adaptive Neuro Fuzzy Inference System (ANFIS) with hybrid learning method give high prediction for rainfall as a forecasting model. Thus, in this study we investigate the efficient membership function of ANFIS for predicting rainfall in Banyuwangi, Indonesia. The number of different membership functions that use hybrid learning method is compared. The validation process shows that 3 or 4 membership function gives minimum RMSE results that use temperature, wind speed and relative humidity as parameters.

Developing Adaptive Neuro-Fuzzy Inference System for Controlling the Intelligent Universal Transformers in ADA

2012 ◽  
Vol 433-440 ◽  
pp. 3969-3973
Author(s):  
Maryam Sadeghi ◽  
Majid Gholami
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Voltage Regulation ◽  
Current Control ◽  
Distribution Automation ◽  
Membership Functions ◽  
Back Propagation Algorithm ◽  
Input Output ◽  
Inference System ◽  
Neuro Fuzzy

This approach is carry out for developing the Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling the forthcoming Intelligent Universal Transformer (IUT) in regard of voltages and current control in both input and output stages which is optimized by particle swarm optimization. Current or voltages errors and their time derivative have been considered as the inputs of Nero Fuzzy controller for elaborating the firing angles of converters in IUT basic construction. ANFIS constructed from a fuzzy inference system (FIS) in which the membership function parameters are tuned according to the back propagation algorithm or in conjunction to the least squares method. A neural network maps inputs through input membership functions and associated parameters, and output membership functions and associated parameters to outputs which interprets the input-output map. The associated parameters of membership functions change through the learning algorithm by a gradient vector modeling the input output data in case of given parameters. Optimization method will be investigated to adjust the parameters according to error reduction computed by sum of the squared variation from actual outputs to the desired ones. Advanced Distribution Automation (ADA) is the state of art introducing for tomorrows distribution automation with the new invention in management and control. ADA is equipping by the Intelligent Equipment Devices (IED) in which IUT is a key point introducing as an intelligent transformer subjecting for tomorrows distribution automation in the near future. The proposed ANFIS is a control scheme develop for controlling the IUT by bringing the major advantages like harmonic Filtering, voltage regulation, automatic sag correction, energy storage, 48V DC option, three phase outputs in term of one phase input, reliable divers power as 240V 400HZ for communication utilization and two other 240V 60 HZ outputs, dynamic system monitoring and robustness in major disturbances occurred in terms of input and load variation.

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