scholarly journals Modelling Unconfined Groundwater Recharge Using Adaptive Neuro-Fuzzy Inference System

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1280
Author(s):  
Khaled Mohamed Nabil I. Elsayed ◽  
Rabee Rustum ◽  
Adebayo J. Adeloye
Keyword(s):  
Groundwater Recharge ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Monitoring Site ◽  
Data Set ◽  
Inference System ◽  
The North ◽  
Neuro Fuzzy ◽  
Ground Water Recharge ◽  
Human Thinking

Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable; hence, it is recommended to be used for similar applications.

The Adaptive Neuro-Fuzzy Inference System (ANFIS) Application for the Ammonium Removal from Aqueous Solution Predicting by Biochar

2018 ◽  
Vol 931 ◽  
pp. 985-990
Author(s):  
Ahmed S. Khalil ◽  
Sergey V. Starovoytov ◽  
Nikolai S. Serpokrylov
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Pilot Scale ◽  
Fish Farms ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Ammonium Removal ◽  
Neuro Fuzzy

The adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the removal of ammonium () from wastewater. The ANFIS model was developed and validated with a data set from a pilot-scale of adsorption system treating aqueous solutions and wastewater from fish farms. The data sets consist of four parameters, which include pH, temperature, an initial concentration of ammonium and amount of adsorbent. The adsorbent was biochar obtained from rice straw. The ANFIS models performance was assessed through the root mean absolute error (RMSE) and was validated by testing data. The results of the study show that the adaptive neuro-fuzzy inference system (ANFIS) is able to predict the percentage of ammonium removal from adsorption column according to the input variables with acceptable accuracy, suggesting that the adaptive neuro-fuzzy inference system model is a valuable tool for estimating the quality of fish farms water. This model of ANFIS leads to cost reduction because prediction can be done without resorting to efforts that require cost and time.

scholarly journals The development of Adaptive Neuro-Fuzzy Inference System model to diagnosis diabetes disease data set

MATEMATIKA ◽  
2017 ◽  
Vol 33 (1) ◽  
pp. 11
Author(s):  
Mamman Mamuda ◽  
Saratha Sathasivan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Disease Diagnosis ◽  
Learning Ability ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Linguistic Rules ◽  
Gradient Descent Algorithm

Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.

scholarly journals Robustness of Adaptive Neuro- Fuzzy Inference System for Optimal Prediction using Roulette Wheel Method

2020 ◽  
Vol 8 (5) ◽  
pp. 380-384
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Optimal Prediction ◽  
Data Set ◽  
Inference System ◽  
Roulette Wheel ◽  
Neuro Fuzzy ◽  
Support Decision Making

An information system that supports automatic decision making with help of intelligent system by computerized manner. The proposed work has been developed and deployed a robust method is contributed to decision making in medical system and the diagnosis the major risk of the patients in earlier. The main goal of the proposed research is to develop data mining techniques to support decision making and to control the controllable risk factors and also overcome the other parts of organs highly affected by diabetes, kidney disease, heart condition and which in turn reduces the risk of the patients. Robustness of Adaptive Neuro-Fuzzy Inference System (RANFIS) designed a fuzzy inference system (FIS) to enrich the knowledge about the data set whose membership function parameters can be altered randomly by the process of mutation.

scholarly journals AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FORECASTING AUSTRALIA’S DOMESTIC LOW COST CARRIER PASSENGER DEMAND

Aviation ◽  
2015 ◽  
Vol 19 (3) ◽  
pp. 150-163 ◽  
Author(s):  
Panarat Srisaeng ◽  
Glenn S. Baxter ◽  
Graham Wild
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Low Cost ◽  
Percentage Error ◽  
Subtractive Clustering ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Partition Method

This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

scholarly journals The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Muna A Alzukrah ◽  
Yosof M Khalifa
Keyword(s):  
Solar Radiation ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Global Solar Radiation ◽  
Percentage Error ◽  
Data Set ◽  
Inference System ◽  
Energy Applications ◽  
Neuro Fuzzy

The prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly global solar radiation on a horizontal surface in Libya. The real meteorological solar radiation data from 5 stations for the period of 1982 - 2009 with diffrent latitudes and longitudes were used in the current study. The data set is divided into two subsets; the fist is used for training and the latter is used for testing the model. (ANFIS) combines fuzzy logic and neural network techniques that are used in order to gain more effiency. The statistical performance parameters such as root mean square error (RMSE), mean absolute percentage error (MAPE) and the coeffient of effiency (E) were calculated to check the adequacy of the model. On the basis of coeffient of effiency, as well as the scatter diagrams and the error modes, the predicted results indicate that the neuro-fuzzy model gives reasonable results: accuracy of about 92% - 96% and the RMSE ranges between 0.22 - 0.35 kW.hr/m2/day

Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands

2014 ◽  
Vol 71 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Mawuli Dzakpasu ◽  
Miklas Scholz ◽  
Valerie McCarthy ◽  
Siobhán Jordan ◽  
Abdulkadir Sani
Keyword(s):  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Real Time Monitoring ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Simulated Effluent ◽  
Input Variables

Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems.

Bus service quality prediction and attribute ranking using probabilistic neural network and adaptive neuro fuzzy inference system

2016 ◽  
Vol 43 (9) ◽  
pp. 822-829 ◽  
Author(s):  
Rokibul Islam ◽  
Sarder Rafee Musabbir ◽  
Irfan Uddin Ahmed ◽  
Md. Hadiuzzaman ◽  
Mehedi Hasnat ◽  
...  
Keyword(s):  
Neural Network ◽  
Service Quality ◽  
Prediction Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Probabilistic Neural Network ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Bus Service

This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership.

scholarly journals PEMODELAN KECEPATAN ANGIN DI KOTA SEMARANG MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)

2019 ◽  
Vol 8 (3) ◽  
pp. 296-304
Author(s):  
Alifah Zahlevi ◽  
Alan Prahutama ◽  
Abdul Hoyyi
Keyword(s):  
Wind Velocity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
North Coast ◽  
Strong Wind ◽  
Residual Distribution ◽  
Anfis Model ◽  
Inference System ◽  
The North ◽  
Neuro Fuzzy

Semarang city is the one of the strategic areas located in the middle of the north coast of Java that has a tropical climate with the high humidity and temperature, so it often causes a high rainfall and strong wind. So that is way Semarang city is ever sustained the extreme weather like a Tropical Storm. Since January 2016 until 2017 there are 34 cases of Tornado and 24 incidents of fallen trees because of the gale. For helping the people to be allert the effect of the strong winds can be done by predicting the average of wind velocity by using Adaptive Neuro-Fuzzy Inference System (ANFIS) method which can predict the climate change that do not require the assumption of white noise and normal residual distribution. In addition ANFIS is a group of neural network with input that has been fuzzied on the first or second layer, but the weight of the artificial neural is not fuzzied. The identification result of stationaries obtained the plot of PACF on the first and second lag, with the result that these lag which will be a input variable on ANFIS model. The result of ANFIS by using cluster FCM, the third total membership show the smallest percentage of RMSE in-sample is 0,0048 on the first lag, and the smallest percentage of RMSE out-sample is 0,008 on the ANFIS model with the input lag 1 and three cluster. Keywords : the average of wind velocity, ANFIS, RMSE

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