Prediction of Longshore Sediment Transport Using Soft Computing Techniques

2008 ◽  
Author(s):  
Roham Bakhtyar ◽  
David Andrew Barry ◽  
Abbas Ghaheri
Keyword(s):  
Sediment Transport ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Transport Rate ◽  
Breaking Wave ◽  
Input Output ◽  
Empirical Formulas ◽  
Inference System ◽  
Longshore Sediment Transport ◽  
Soft Computing Techniques

An important task for coastal engineers is to predict the sediment transport rates in coastal regions with correct estimation of this transport rate, it is possible to predict both natural morphological or beach morphology changes and the influence of coastal structures on the coast line. A large number of empirical formulas have been proposed for predicting the longshore sediment transport rate as a function of breaking wave characteristics and beach slope. The main shortcoming of these empirical formulas is that these formulas are not able to predict the field transport rate accurately. In this paper, an Adaptive-Network-Based Fuzzy Inference System which can serve as a basis for consulting a set of fuzzy IF-THEN rules with appropriate membership functions to generate the stipulated input-output pairs, is used to predict and model longshore sediment transport. For statistical comparison of predicted and observed sediment transport, bias, Root Mean Square Error, and scatter index are used. The results suggest that the ANFIS method is superior to empirical formulas in the modeling and forecasting of sediment transport. We conclude that the constructed models, through subtractive fuzzy clustering, can efficiently deal with complex input-output patterns. They can learn and build up a neuro-fuzzy inference system for prediction, while the forecasting results provide a useful guidance or reference for predicting longshore sediment transport.

Longshore sediment transport estimation using a fuzzy inference system

2008 ◽  
Vol 30 (4) ◽  
pp. 273-286 ◽  
Author(s):  
R. Bakhtyar ◽  
A. Ghaheri ◽  
A. Yeganeh-Bakhtiary ◽  
T.E. Baldock
Keyword(s):  
Sediment Transport ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Longshore Sediment Transport

Cross-shore sediment transport estimation using fuzzy inference system in the swash zone

2011 ◽  
Vol 348 (8) ◽  
pp. 2005-2025 ◽  
Author(s):  
R. Bakhtyar ◽  
A. Ghaheri ◽  
A. Yeganeh-Bakhtiary ◽  
D.-S. Jeng
Keyword(s):  
Sediment Transport ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Swash Zone ◽  
Inference System

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.

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.

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 Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

2020 ◽  
Author(s):  
Sina Ardabili ◽  
Bertalan Beszedes ◽  
Laszlo Nadai ◽  
Karoly Szell ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Air Conditioning ◽  
High Performance ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Save Energy

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

scholarly journals NEURO-FUZZY APPROACH TO PREDICTIVE MODELING OF EMISSIONS FROM BIODIESEL POWERED TRANSIT BUSES

Transport ◽  
2012 ◽  
Vol 26 (4) ◽  
pp. 334-352 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan ◽  
Abhisek Mudgal ◽  
Shauna Hallmark
Keyword(s):  
Soft Computing ◽  
Real World ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Inference System ◽  
Measuring Device ◽  
Transit Buses ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The rise in freight passenger transportation is responsible for air pollution, green house gas emissions (especially CO2) and high fuel demand. New engine technology and fuels are discovered and tested throughout the world. Biodiesel, an alternative for diesel, has been seen as a solution. However, the amount of emissions generated by a biodiesel fueled vehicle has not been understood well since most research studies of this kind reported in the literature were conducted in the laboratory. In the present study, emissions (NOx, HC, CO, CO2 and PM) were measured from biodiesel fueled transit buses using an on-road emissions measuring device known as the Portable Emissions Measurement System (PEMS). On-road study is important in terms of understanding the amount of emissions generated under the real traffic and environmental conditions. Emissions were measured on buses fueled with regular diesel (B0), B10 blend (10% biodiesel + 90% diesel) and B20 blend (20% biodiesel + 80% diesel). This paper demonstrates the use of hybrid soft-computing techniques such as the neuro-fuzzy technique for developing emissions prediction models from real-world data. Hybrid soft-computing techniques have been shown to work well in handling data prone to noise and uncertainty, which is characteristic of real-world scenario. Two neuro-fuzzy methodologies were considered in this study: the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS). A brief review of model development, recommended parametric settings, and statistical evaluation of prediction performance of both techniques are discussed. In general, the ANFIS showed better prediction accuracy for the individual emissions compared to DENFIS although the prediction accuracies are comparable.

Performance assessment of permeability index prediction in an ironmaking process via soft computing techniques

2016 ◽  
Vol 231 (6) ◽  
pp. 1101-1113 ◽  
Author(s):  
Yasin Tunckaya
Keyword(s):  
Blast Furnace ◽  
Liquid Metal ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Permeability Index ◽  
Inference System ◽  
Soft Computing Techniques ◽  
Ironmaking Process ◽  
Neural Fuzzy

Permeability index is a crucial productivity indicator of the lower zone in blast furnaces to maintain the operation, energy consumption, and hot liquid metal production rates during the ironmaking process. Blast furnace operation parameters such as coke-to-ore ratio, wall pressures and temperatures, flame temperature, top gas pressure, temperature and composition, hot blast pressure and temperature, sounding levels, etc. and also the level of hot liquid metal and slag in the bottom of furnace, influence the permeability phenomenon directly. Hence, fluctuations and instantenous variations of permeability index parameter should be avoided by controlling inadequate drainage cycles and operational factors to achieve more efficient and stable operation in the furnaces. In this study, permeability index parameter of the Erdemir Blast Furnace #1, located in Turkey, is modeled and experimental computing work is carried out to assess the operation performance of the furnace, depending on selected input parameters. The demanding artificial intelligence and soft computing techniques, artificial neural networks and adaptive neural fuzzy inference system, and a well-known statistical tool, autoregressive integrated moving average model are executed throughout the study using previous furnace data, received during one day of operation. Selected performance measures, coefficient of determination ( R2) and root mean squared error, are used to compare the forecasting accuracy of proposed models. Consequently, the most satisfactory forecasting model of the study, adaptive neural fuzzy inference system, is proposed to be integrated into the plant control system as an expert modeler.

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