scholarly journals Efficacy of ANFIS-GOA technique in flood prediction: a case study of Mahanadi river basin in India

2021 ◽  
Author(s):  
Abinash Sahoo ◽  
Sandeep Samantaray ◽  
Siddhartha Paul
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Large River ◽  
Anfis Model ◽  
Flood Prediction ◽  
Inference System ◽  
Discharge Data ◽  
Squared Error ◽  
Model Complex ◽  
Grasshopper Optimization Algorithm

Abstract Accurateness in flood prediction is of utmost significance for mitigating catastrophes caused by flood events. Flooding leads to severe civic and financial damage, particularly in large river basins, and mainly affects the downstream regions of a river bed. Artificial Intelligence (AI) models have been effectively utilized as a tool for modeling numerous nonlinear relationships and is suitable to model complex hydrological systems. Therefore, the main purpose of this research is to propose an effective hybrid system by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with meta-heuristic Grey Wolf Optimization (GWO) and Grasshopper Optimization Algorithm (GOA) for flood prediction in River Mahanadi, India. Robustness of proposed meta-heurestics are assessed by comparing with a conventional ANFIS model focusing on various input combinations considering 50 years of monthly historical flood discharge data. The potential of the AI models is evaluated and compared with observed data in both training and validation sets based on three statistical performance evaluation factors, namely root mean squared error (RMSE), mean squared error (MSE) and Wilmott Index (WI). Results reveal that robust ANFIS-GOA outperforms standalone AI techniques and can make superior flood forecasting for all input scenarios.

scholarly journals Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

2019 ◽  
Vol 9 (18) ◽  
pp. 3841 ◽  
Author(s):  
Ly ◽  
Pham ◽  
Dao ◽  
Le ◽  
Le ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Principal Component ◽  
Absolute Error ◽  
Anfis Model ◽  
Natural Sand ◽  
Inference System ◽  
Computing Technique ◽  
Manufactured Sand

Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

Multilayer Optimization Approach for Fuzzy Systems

2011 ◽  
pp. 1121-1129
Author(s):  
Ivan N. Silva ◽  
Rogerio A. Flauzino
Keyword(s):  
Energy Function ◽  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Energy Functions ◽  
Inference System ◽  
Squared Error ◽  
The Mean ◽  
Inference Systems

The design of fuzzy inference systems comes along with several decisions taken by the designers since is necessary to determine, in a coherent way, the number of membership functions for the inputs and outputs, and also the specification of the fuzzy rules set of the system, besides defining the strategies of rules aggregation and defuzzification of output sets. The need to develop systematic procedures to assist the designers has been wide because the trial and error technique is the unique often available (Figueiredo & Gomide, 1997). In general terms, for applications involving system identification and fuzzy modeling, it is convenient to use energy functions that express the error between the desired results and those provided by the fuzzy system. An example is the use of the mean squared error or normalized mean squared error as energy functions. In the context of systems identification, besides the mean squared error, data regularization indicators can be added to the energy function in order to improve the system response in presence of noises (from training data) (Guillaume, 2001). In the absence of a tuning set, such as happens in parameters adjustment of a process controller, the energy function can be defined by functions that consider the desired requirements of a particular design (Wan, Hirasawa, Hu & Murata, 2001), i.e., maximum overshoot signal, setting time, rise time, undamped natural frequency, etc. From this point of view, this article presents a new methodology based on error backpropagation for the adjustment of fuzzy inference systems, which can be then designed as a three layers model. Each one of these layers represents the tasks performed by the fuzzy inference system such as fuzzification, fuzzy rules inference and defuzzification. The adjustment procedure proposed in this article is performed through the adaptation of its free parameters, from each one of these layers, in order to minimize the energy function previously specified. In principle, the adjustment can be made layer by layer separately. The operational differences associated with each layer, where the parameters adjustment of a layer does not influence the performance of other, allow single adjustment of each layer. Thus, the routine of fuzzy inference system tuning acquires a larger flexibility when compared to the training process used in artificial neural networks. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, such methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno.

Thermal Errors Modeling of a CNC Machine’s Axis Using Neural Network and Fuzzy Logic

2011 ◽  
Vol 110-116 ◽  
pp. 2976-2982 ◽  
Author(s):  
Sina Eskandari ◽  
Behrooz Arezoo ◽  
Amir Abdullah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Previous Method ◽  
Data Set ◽  
Inference System ◽  
Thermal Errors ◽  
Squared Error

Thermal errors of CNC machines have significant effects on precision of a workpiece. One of the approaches to reduce these errors is modeling and on-line compensating them. In this study, thermal errors of an axis of the machine are modeled by means of artificial neural networks along with fuzzy logic. Models are created using experimental data. In neural networks modeling, MLP type which has 2 hidden layers is chosen and it is trained by backpropagation algorithm. Finally, the model is validated with the aid of calculating mean squared error and correlation coefficients between outputs of the model and a checking data set. On the other hand, an adaptive neuro-fuzzy inference system is utilized in fuzzy modeling which uses neural network to develop membership functions as fuzzifiers and defuzzifiers. This network is trained by hybrid algorithm. At the end, model validation is done by mean squared error like previous method. The results show that the errors of both modeling techniques are acceptable and models can predict thermal errors reliably.

scholarly journals A hybrid model for modelling the salinity of the Tafna River in Algeria

2019 ◽  
Vol 40 (1) ◽  
pp. 127-135 ◽  
Author(s):  
Khemissi Houari ◽  
Tarik Hartani ◽  
Boualem Remini ◽  
Abdelouhab Lefkir ◽  
Leila Abda ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Inference System ◽  
Squared Error ◽  
Good Agreement

Abstract In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing).

scholarly journals A hybrid model based on mBA-ANFIS for COVID 19 confirmed cases prediction and forecast

2020 ◽  
Author(s):  
Sohail Saif ◽  
Priya Das ◽  
Suparna Biswas
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Bees Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Hybrid Forecasting

Abstract In India, the first confirmed case of novel corona virus (COVID-19) was discovered on 30 January, 2020. The number of confirmed cases is increasing day by day and it crossed 21,53,010 on 09 August, 2020. In this paper a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed modelis based on adaptive neuro-fuzzy inference system (ANFIS) and mutation based Bees Algorithm (mBA). ThemetaheuristicBees Algorithm (BA) has been modified applying 4 types of mutation and Mutation based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USAand the number of confirmed cases in next 10 days in Indiahas been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results showthat the proposed model has achieved better performance in terms of Root Mean squared error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE).It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data.Similarly, for USA the values are 4468.72, 3082.07, 6.1, 0.000952 for RMSE, MAE, MAPE, NRMSE respectively.

scholarly journals Performance Evaluation of ANFIS and GA-ANFIS for Predicting Stock Market Indices

2021 ◽  
Vol 13 (7) ◽  
pp. 1
Author(s):  
Farnaz Ghashami ◽  
Kamyar Kamyar
Keyword(s):  
Genetic Algorithm ◽  
Stock Market ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
The United States ◽  
Coefficient Of Determination ◽  
Improve Performance ◽  
Inference System ◽  
Squared Error ◽  
Neuro Fuzzy

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the model is tested on the NASDAQ stock market indices which is among the most widely followed indices in the United States. Empirical results show that by determining the parameters of ANFIS (premise and consequent parameters) using GA, we can improve performance in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R-Squared) in comparison with using solely ANFIS.

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

2012 ◽  
Vol 57 (4) ◽  
pp. 933-943
Author(s):  
Abbas Aghajani Bazzazi ◽  
Mohammad Esmaeili
Keyword(s):  
Fuzzy Inference System ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Open Pit ◽  
Good Prediction ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Good Prediction Performance ◽  
Predicted Values

Abstract Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

The Research of Error Compensation for the 3D Surface Measurement

2012 ◽  
Vol 482-484 ◽  
pp. 2192-2196
Author(s):  
Yuan Tian ◽  
Zi Ma ◽  
Peng Li
Keyword(s):  
Measurement Error ◽  
Fuzzy Inference System ◽  
Error Compensation ◽  
Point Cloud ◽  
Fuzzy Inference ◽  
Surface Measurement ◽  
Anfis Model ◽  
Inference System ◽  
Surface Error ◽  
3D Surface

For improving precision of 3D surface measurement equipments, which are playing important role in reverse engineering, the Adaptive Network based Fuzzy Inference System (ANFIS) is developed to reconstruct 3D surface error, and the measurement error of point cloud is compensated by the presented 3D error ANFIS model. The precision of 3D surface measurement equipments has been improved noticeably

scholarly journals Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System

2020 ◽  
Vol 11 (1) ◽  
pp. 203
Author(s):  
Primož Jelušič ◽  
Andrej Ivanič ◽  
Samo Lubej
Keyword(s):  
Fuzzy Inference System ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Ground Vibration ◽  
Residential Building ◽  
Ground Vibrations ◽  
Adaptive Network ◽  
Inference System ◽  
Fast Learning

Efforts were made to predict and evaluate blast-induced ground vibrations and frequencies using an adaptive network-based fuzzy inference system (ANFIS), which has a fast-learning capability and the ability to capture the non-linear response during the blasting process. For this purpose, the ground vibrations generated by the blast in a tunnel tube were monitored at a residential building located directly above the tunnel tube. To investigate the usefulness of this approach, the prediction by the ANFIS was also compared to those by three of the most commonly used vibration predictors. The efficiency criteria chosen for the comparison between the predicted and actual data were the sum of squares due to error (SSE), the root mean squared error (RMSE), and the goodness of fit (R-squared and adjusted R-squared). The results show that the ANFIS prediction model performs better than the commonly used predictors.

scholarly journals Classification of Gait Patterns in Patients with Neurodegenerative Disease Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Ye ◽  
Yi Xia ◽  
Zhiming Yao
Keyword(s):  
Motor Neurons ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pso Algorithm ◽  
Anfis Model ◽  
Inference System ◽  
Gait Dynamics ◽  
Double Support ◽  
Adaptive Capabilities ◽  
Neuro Fuzzy

A common feature that is typical of the patients with neurodegenerative (ND) disease is the impairment of motor function, which can interrupt the pathway from cerebrum to the muscle and thus cause movement disorders. For patients with amyotrophic lateral sclerosis disease (ALS), the impairment is caused by the loss of motor neurons. While for patients with Parkinson’s disease (PD) and Huntington’s disease (HD), it is related to the basal ganglia dysfunction. Previously studies have demonstrated the usage of gait analysis in characterizing the ND patients for the purpose of disease management. However, most studies focus on extracting characteristic features that can differentiate ND gait from normal gait. Few studies have demonstrated the feasibility of modelling the nonlinear gait dynamics in characterizing the ND gait. Therefore, in this study, a novel approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for identification of the gait of patients with ND disease. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. Gait dynamics such as stride intervals, stance intervals, and double support intervals were used as the input variables to the model. The particle swarm optimization (PSO) algorithm was utilized to learn the parameters of the ANFIS model. The performance of the system was evaluated in terms of sensitivity, specificity, and accuracy using the leave-one-out cross-validation method. The competitive classification results on a dataset of 13 ALS patients, 15 PD patients, 20 HD patients, and 16 healthy control subjects indicated the effectiveness of our approach in representing the gait characteristics of ND patients.

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