scholarly journals Artificial Intelligence Based Modelling of Adsorption Water Desalination System

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1674
Author(s):  
Hesham Alhumade ◽  
Hegazy Rezk ◽  
Abdulrahim A. Al-Zahrani ◽  
Sharif F. Zaman ◽  
Ahmed Askalany
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Research Work ◽  
Water Desalination ◽  
Coefficient Of Performance ◽  
Coefficient Of Determination ◽  
Cooling Power ◽  
Anfis Model ◽  
Inference System ◽  
Output Performance

The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

scholarly journals Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Materials ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1670 ◽  
Author(s):  
Lu Minh Le ◽  
Hai-Bang Ly ◽  
Binh Thai Pham ◽  
Vuong Minh Le ◽  
Tuan Anh Pham ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Buckling Load ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Steel Columns ◽  
Neuro Fuzzy ◽  
Hybrid Artificial Intelligence

This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

scholarly journals Evaluation of different types of artificial intelligence methods to model the suspended sediment load in Tigris River

2018 ◽  
Vol 162 ◽  
pp. 03003 ◽  
Author(s):  
Mustafa Al-Mukhtar
Keyword(s):  
Artificial Intelligence ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Suspended Sediment Load ◽  
Inference System ◽  
Tigris River ◽  
Artificial Neural Network Ann

Modeling of suspended sediment load in rivers has a major role in a proper management of water resources. Artificial intelligence has been identified as an efficient way to model the complex nonlinear hydrological relationship. In this study, Adaptive Neuro Fuzzy Inference System (ANFIS), in addition to two different kinds of Artificial Neural Network (ANN) i.e. feedforward and radial basis networks were used and compared to model the suspended sediment load (SSL) in Tigris River-Baghdad using the streamflow discharge as input. To this end, an intermittent data of SSL and streamflow were collected over the period 1962-1981 from Sarai station in Baghdad. 70 % of these data was used to calibrate (train) the networks and the remaining 30% for the validation (test). The coefficient of determination (R2), root mean square error (RMSE), and Nash and Sutcliffe model efficiency coefficient (NSE) were used to judge whether the observed and modelled data belong to the same distribution. Results revealed that the ANFIS model outperform the other methods. R2, RMSE, and NSE of ANFIS during the calibration phase were equal to 0.58, 75617, and 0.58, respectively and during the validation were 0.72, 27944, and 0.59, respectively. Therefore, ANFIS approach is recommended to estimate the river suspended sediment load.

Investigating Entry Capacity Models of Roundabouts under Heterogeneous Traffic Conditions

2018 ◽  
Vol 2672 (15) ◽  
pp. 35-43 ◽  
Author(s):  
Ashish Kumar Patnaik ◽  
Ankit Raj Ranjan ◽  
Prasanta Kumar Bhuyan
Keyword(s):  
Fuzzy Inference ◽  
Point Of View ◽  
Heterogeneous Traffic ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Anfis Model ◽  
Inference System ◽  
Critical Gap ◽  
Traffic Conditions ◽  
Traffic Volumes

The primary objectives of this study are to develop two roundabout entry capacity models using a regression-based multiple non-linear regression model (MNLR) and artificial intelligence (AI)-based ANFIS (adaptive neuro-fuzzy inference system) model under heterogeneous traffic conditions. ANFIS is the latest technique in the field of AI that integrates both neural networks and fuzzy logic principles in a single framework. Required data have been collected from 27 roundabouts in eight states of India. To assess the significance of these models and select the best model among them, modified rank index is applied in this study. The coefficient of determination ( R2) and Nash–Sutcliffe model efficiency coefficient ‘ E’ values are found to be 0.92, 0.91 and 0.98, 0.98 for the MNLR and ANFIS model, respectively. The ANFIS model is found to be the best model in this study. However, from a practical point of view, the MNLR model is recommended for determining roundabout entry capacity under heterogeneous traffic conditions. Sensitivity analysis reports that critical gap is the prime variable and shares 18.43% for the development of roundabout entry capacity. As compared with the Girabase formula (France), Brilon wu formula (Germany), and HCM 2010 models, the proposed MNLR model is quite reliable under low to medium ranges of traffic volumes.

scholarly journals An alternative approaches to predict flashover voltage on polluted outdoor insulators using artificial intelligence techniques

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Ali. A. Salem ◽  
Rahisham Abd Rahman ◽  
M. S. Kamarudin ◽  
N. A. Othman ◽  
N. A. M. Jamail ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Theoretical Modelling ◽  
Coefficient Of Determination ◽  
Critical Voltage ◽  
Bottom Surface ◽  
Salt Deposit ◽  
Inference System ◽  
Flashover Voltage ◽  
Deposit Density

This paper presents an alternative approach for predicting critical voltage of pollution flashover by using Artificial Intelligence (AI) technique. Data from experimental works combined with the theoretical results from well-known theoretical modelling are used to derive algorithm for Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) for determining critical voltage of flashover. Series of laboratory testing and measurement are carried for 1:1, 1:5 and 1:10 ratios of top to bottom surface salt deposit density on cup and pin insulators. Insulators variables such as height H, diameter D, form factor F, creepage distance L, equivalent salt deposit density (ESDD) and flashover voltage correction are identified and used to train the AI network. Comparative studies have evidently shown that the proposed (AI) technique gives the satisfactory results compared to the analytical model and test data with the Coefficient of determination R-Square value of more than 97%.

scholarly journals Modeling of Carbon Dioxide Fixation Rate by Micro Algae Using Hybrid Artificial Intelligence and Fuzzy Logic Methods and Optimization by Genetic Algorithm

2021 ◽  
Author(s):  
Omkar Singh Kushwaha ◽  
Haripriyan Uthayakumar ◽  
Karthigaiselvan Kumaresan
Keyword(s):  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Fuzzy Inference ◽  
Scale Up ◽  
Hybrid Approach ◽  
Coefficient Of Determination ◽  
Anfis Model ◽  
Fixation Rate ◽  
Inference System ◽  
Prediction Capability

Abstract In this study we are reporting a prediction model for the estimation of carbon dioxide (CO2) fixation based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) hybrid approach. The experimental parameters such as temperature and pH conditions of the micro-algae-based carbon dioxide uptake process were taken as the input variables and the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) algorithm in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as RMSE, R2 and AARD. According to the analysis, GA-ANFIS model depicted a superior prediction capability over ANFIS optimized model. The Root Mean Square Error (RMSE), coefficient of determination (R2) and AARD for GA-ANFIS were determined as 0.000431, 0.97865 and 0.044354 in the training phase and 0.00056, 0.98457 and 0.032156 in the testing phase, respectively for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having very high potential to accurately calculate CO2 fixation rate and the exploration of the industrial scale-up process for commercial activities.

scholarly journals Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS models

2018 ◽  
Vol 20 (3) ◽  
pp. 589-597 ◽  
Keyword(s):  
Fuzzy Inference ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Anfis Model ◽  
Inference System ◽  
Heating Value ◽  
Wide Range ◽  
Hhv Prediction ◽  
Prediction Capability

<p>In this study, a new model for biomass higher heating value (HHV) prediction based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was proposed. Proximate analysis (volatile matter, fixed carbon and ash content) data for a wide range of various biomass types from the literature were used as input in model studies. Optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm in order to achieve optimum prediction capability. The best-fitting model was selected using statistical analysis tools. According to the analysis, PSO-ANFIS model showed a superior prediction capability over ANFIS and GA optimized ANFIS model. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE) and coefficient of determination (R2) for PSO-ANFIS were determined as 0.3138, 0.2545, -0.00129 and 0.9791 in the training phase and 0.3287, 0.2748, 0.00120 and 0.9759 in the testing phase, respectively. As a result, it can be concluded that the proposed PSO-ANFIS model is an efficient technique and has potential to calculate biomass HHV prediction with high accuracy.</p>

scholarly journals Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Gurmanik Kaur ◽  
Ajat Shatru Arora ◽  
Vijender Kumar Jain
Keyword(s):  
Prediction Models ◽  
Fuzzy Inference ◽  
Research Work ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Inference System ◽  
Forward Stepwise ◽  
Artificial Neural Network Ann

Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS-SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS-SVM (PCA-LS-SVM) model has the highest prediction accuracy with coefficient of determination (R2) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R2 = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R2 = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies.

scholarly journals Modelling and Prediction of Water Quality by Using Artificial Intelligence

2021 ◽  
Vol 13 (8) ◽  
pp. 4259
Author(s):  
Mosleh Hmoud Al-Adhaileh ◽  
Fawaz Waselallah Alsaade
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Fuzzy Inference ◽  
Feed Forward Neural Network ◽  
Statistical Parameters ◽  
Anfis Model ◽  
K Nearest Neighbors ◽  
Inference System ◽  
Efficient Operation ◽  
Sanitation Systems

Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.

scholarly journals An artificial intelligence approach for concrete hardened property estimation

2020 ◽  
Vol 14 (2) ◽  
pp. 40-52 ◽  
Author(s):  
Tu Trung Nguyen ◽  
Kien Dinh
Keyword(s):  
Artificial Intelligence ◽  
Sensitivity Analysis ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fine Aggregate ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Compressive Strength Of Concrete

An alternative method using Artificial Intelligence (AI) to predict the 28-day strength of concrete from its primary ingredients is presented in this research. A series of 424 data samples collected from a previous study were employed for developing, testing, and validation of Adaptive Neuro-Fuzzy Inference System (ANFIS) models. Seven mix parameters, namely Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer, Coarse Aggregate, and Fine Aggregate were used as the inputs of the models while the output was the 28-day compressive strength of concrete. In the first step, different models with various input membership functions were explored and compared to obtain an optimal ANFIS model. In the second step, that model was utilized to predict the compressive strength value for each concrete sample, and to compare with those obtained from the compressive test in laboratory. The results showed that the selected ANFIS model can be used as a reliable tool for predicting the compressive strength of concrete with Root Mean Squared Error values of 5.97 MPa and 7.73 MPa, respectively, for the training and test sets. In addition, the sensitivity analysis results revealed that the accuracy of the proposed model improved with an increase in the number of input parameters/variables. Keywords: artificial intelligence; adaptive neuro-fuzzy inference system; concrete strength; sensitivity analysis.

scholarly journals Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020

2020 ◽  
Vol 17 (11) ◽  
pp. 4076 ◽  
Author(s):  
Abdallah Alsayed ◽  
Hayder Sadir ◽  
Raja Kamil ◽  
Hasan Sari
Keyword(s):  
Infection Rate ◽  
Fuzzy Inference ◽  
Basic Reproductive Number ◽  
Coefficient Of Determination ◽  
Reproductive Number ◽  
Percentage Error ◽  
Health Providers ◽  
Anfis Model ◽  
Inference System ◽  
The Government

The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible–Exposed–Infectious–Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July–11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

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