scholarly journals Comparison of neural networks and neuro-fuzzy computing techniques for prediction of peak breach outflow

2016 ◽  
Vol 18 (4) ◽  
pp. 724-740 ◽  
Author(s):  
Hasan G. Elmazoghi ◽  
Vail Karakale (Waiel Mowrtage) ◽  
Lubna S. Bentaher
Keyword(s):  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Data Set ◽  
Inference System ◽  
Embankment Dams ◽  
Small Dams ◽  
Neuro Fuzzy

Accurate prediction of peak outflows from breached embankment dams is a key parameter in dam risk assessment. In this study, efficient models were developed to predict peak breach outflows utilizing artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Historical data from 93 embankment dam failures were used to train and evaluate the applicability of these models. Two scenarios were applied with each model by either considering the whole data set without classification or classifying the set into small dams (48 dams) and large dams (45 dams). In this way, nine models were developed and their results were compared to each other and to the results of the best available regression equations and recent gene expression programming. Among the different models, the ANFIS model of the first scenario exhibited better performance based on its higher efficiency (E = 0.98), higher coefficient of determination (R2 = 0.98) and lower mean absolute error (MAE = 840.9). Moreover, models based on classified data enhanced the prediction of peak outflows particularly for small dams. Finally, this study indicated the potential of the developed ANFIS and ANN models to be used as predictive tools of peak outflow rates of embankment dams.

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.

Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods

2019 ◽  
Vol 68 (7) ◽  
pp. 573-584 ◽  
Author(s):  
Robabeh Jafari ◽  
Ali Torabian ◽  
Mohammad Ali Ghorbani ◽  
Seyed Ahmad Mirbagheri ◽  
Amir Hessam Hassani
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Support Vector ◽  
Eastern Region ◽  
Data Set ◽  
Inference System ◽  
Groundwater Aquifer ◽  
Tabriz Plain

Abstract Aquifers are one of the largest available freshwater resources. In this paper, total dissolved solids (TDS) of the groundwater aquifer in Tabriz plain is estimated by groundwater physicochemical parameters including Na, HCO3, Ca, Mg, and SO4 in the eastern region of Urmia Lake. For this purpose, four soft computing approaches, namely, multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and gene expression programming (GEP) were used to predict TDS for a period of 10 years (2002–2012). Data were collected from the East Azerbaijan Regional Water Organization, which totaled 1,742 samples. In the application, of the whole data set, 70% (1,220 samples) was used for training and 30% (522 samples) for testing. In the following, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) statistics were used for evaluating the accuracy of the models. According to the results, MLP, ANFIS, SVM, and GEP models could be employed successfully in estimating TDS alterations. A comparison was made between these soft computing approaches that corroborated the superiority of the GEP model over MLP, SVM, and ANFIS models with RMSE = 58.93, R = 0.998, and MAE = 5.21.

scholarly journals Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)

2018 ◽  
Vol 19 (2) ◽  
pp. 392-403 ◽  
Author(s):  
Omolbani Mohammadrezapour ◽  
Jamshid Piri ◽  
Ozgur Kisi
Keyword(s):  
Fuzzy Inference ◽  
Climatic Factors ◽  
Potential Evapotranspiration ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Heuristic Methods ◽  
Inference System

Abstract Evapotranspiration is an important component in planning and management of water resources. It depends on climatic factors and the influence of these factors on each other makes evapotranspiration estimation difficult. This study attempts to explore the possibility of predicting this important component using three different heuristic methods: support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). In this regard, according to the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith equation, the monthly potential evapotranspiration in four synoptic stations (Zahedan, Zabol, Iranshahr, and Chabahar) was calculated using monthly weather data. The weather data were then used as inputs to the SVM, ANFIS and GEP models to estimate potential evapotranspiration. Five different input combinations were tried in the applications. The results of SVM, ANFIS and GEP models were compared based on the coefficient of determination (R2), mean absolute error and root mean square error. Findings showed that the SVM model, whose inputs are average air temperature, relative humidity, wind speed, and sunny hours of the current and one previous month, performed better than the other models for the Zahedan, Zabol, Iranshahr, and Chabahar stations. Comparison of the three heuristic methods indicated that in all stations, the SVM, GEP and ANFIS models took first, second, and third place in estimation of the monthly potential evapotranspiration, respectively.

scholarly journals Implementation of an intelligent clustering methodology for classification of terrorist acts

2019 ◽  
Vol 8 (1) ◽  
pp. 61
Author(s):  
Oluwole Charles Akinyokun ◽  
George Uduak D
Keyword(s):  
Fuzzy Inference ◽  
Absolute Error ◽  
Data Set ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Training Error ◽  
Terrorist Acts ◽  
Threat To Life ◽  
Classification Capability

Terrorist acts have elevated the level of violence, intimidation and pose a threat to life/property, peace and security in the world today. Deployed solutions to curb the occurrence of terrorism prove to be of insignificant value, hence there is the need for more solutions. The research aims at implementing an intelligent clustering methodology for classification of the acts of terrorism in Nigeria. Three experiments were carried out. In the first experiment, the qualitative terrorists data attributes were converted to quantitative attributes using an existing One-of-N (OoN) method and the processed data supplied to Adaptive Neuro-Fuzzy Inference System (ANFIS) (OoN-ANFIS) for training. The second experiment converted the qualitative data attributes to quantitative attributes using the formulated Rank-Frequency-Based (RFB) model before the data was supplied to ANFIS (RFB-ANFIS) for training. In the third experiment, which constitutes the current study, the RFB-processed data was used by Fuzzy C Means (FCM) to generate initial membership values for each point in the data set and then supplied to ANFIS (RFB-FCMANFIS). The results show that RFB-FCMANFIS model generated the least Root Mean Square Error (RMSE), Mean Absolute Error (MAE), training error and checking error of 0.002887, 0.004598, 0.0000713 and 0.0056155 respectively with the highest correlation coefficient of 0.99954, therefore indicating a superior classification capability using the RFB-FCMANFIS. 

scholarly journals Water temperature prediction in a subtropical subalpine lake using soft computing techniques

2016 ◽  
Vol 20 (2) ◽  
pp. 1 ◽  
Author(s):  
Saeed Samadianfard ◽  
Honeyeh Kazemi ◽  
Ozgur Kisi ◽  
Wen-Cheng Liu
Keyword(s):  
Water Temperature ◽  
Soft Computing ◽  
Water Surface ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

Lake water temperature is one of the key parameters in determining the ecological conditions within a lake, as it influences both chemical and biological processes. Therefore, accurate prediction of water temperature is crucially important for lake management. In this paper, the performance of soft computing techniques including gene expression programming (GEP), which is a variant of genetic programming (GP), adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) to predict hourly water temperature at a buoy station in the Yuan-Yang Lake (YYL) in north-central Taiwan at various measured depths was evaluated. To evaluate the performance of the soft computing techniques, three different statistical indicators were used, including the root mean squared error (RMSE), the mean absolute error (MAE), and the coefficient of correlation (R). Results showed that the GEP had the best performances among other studied methods in the prediction of hourly water temperature at 0, 2 and 3 meter depths below water surface, but there was a different trend in the 1 meter depth below water surface. In this depth, the ANN had better accuracy than the GEP and ANFIS. Despite the error (RMSE value) is smaller in ANN than GEP, there is an upper bound in scatter plot of ANN that imposes a constant value, which is not suitable for predictive purposes. As a conclusion, results from the current study demonstrated that GEP provided moderately reasonable trends for the prediction of hourly water temperature in different depths. ResumenLa temperatura del agua es uno de los parámetros básicos para determinar las condiciones ecológicas de un lago, ya que está influenciada por procesos químicos y biológicos. Además, la exactitud en la predicción de la temperatura del agua es esencial para el manejo del lago. En este artículo se evalúa el desempeño de técnicas de soft computing como la Programación de Expresiones de Genes (PEG), que es una variante de la Programación Genética (PG), el Sistema Neuro-fuzzy de Inferencia Adaptativa (Anfis, en inglés) y las Redes Neuronales Artificiales (RNA) para predecir la temperatura del agua en diferentes niveles de una estación flotante del lago Yuan-Yang (YYL), en el centro-norte de Taiwán. Se utilizaron tres indicadores estadísticos, el Error Cuadrático Medio (ECM), el Error Absoluto Medio (MAE, en inglés) y el Coeficiente de Correlación (R) para evaluar el desempeño de las técnicas de computación. Los resultados muestran que la PEG es más exacta en la predicción de la temperatura del agua entre 0,2 y 3 metros de profundidad. Sin embargo, se evidencia una tendencia diferente a partir del metro de profundidad. A esta distancia de la superficie, las RNA son más exactas que la PEG y el Anfis. Los resultados de este estudio probaron claramente la usabilidad del PEG y las RNA en la predicción de la temperatura del agua a diferentes profundidades.

scholarly journals Sea Level Prediction Using Machine Learning

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3566
Author(s):  
Rifat Tur ◽  
Erkin Tas ◽  
Ali Torabi Haghighi ◽  
Ali Danandeh Mehr
Keyword(s):  
Sea Level ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Tide Gauge ◽  
Absolute Error ◽  
Meteorological Factor ◽  
Sea Level Changes ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Cross Correlation Analysis

Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea level changes using sea level height and meteorological factor observations at a tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established to explore the most feasible input combinations for sea level prediction. These scenarios use lagged sea level observations (SC1), and both lagged sea level and meteorological factor observations (SC2) as the input for predictive modeling. Cross-correlation analysis was conducted to determine the optimum input combination for each scenario. Then, several predictive models were developed using linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS) techniques. The performance of the developed models was evaluated in terms of root mean squared error (RMSE), mean absolute error (MAE), scatter index (SI), and Nash Sutcliffe Efficiency (NSE) indices. The results showed that adding meteorological factors as input parameters increases the performance accuracy of the MLR models up to 33% for short-term sea level predictions. Moreover, the results contributed a more precise understanding that ANFIS is superior to MLR for sea level prediction using SC1- and SC2-based input combinations.

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 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.

Deterministic Modeling to Predict the Natural Gas Density Using Artificial Neural Networks

2021 ◽  
Author(s):  
Mariam Shreif ◽  
Shams Kalam ◽  
Mohammad Rasheed Khan ◽  
Rizwan Ahmed Khan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Gas ◽  
Fuzzy Inference ◽  
Ann Model ◽  
Data Set ◽  
Inference System ◽  
Gas Density ◽  
Neuro Fuzzy ◽  
Artificial Neural

Abstract During the past decades, several research studies have been made to unfold the immense and diversified benefits of the innovative applications of machine learning (ML) techniques in the petroleum industry. For instance, machine learning algorithms were applied to estimate the various physical properties of natural gas. Natural gas density is considered an indispensable metric that influences the determination of several variables necessary for analyzing natural gas systems. In this work, the Artificial neural network (ANN), a machine learning technique, was applied to estimate natural gas density incorporating the influencing factors. The ANN model was also compared with another ML technique, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS). A mathematical form has been also presented using ANN. A real data set was taken from the literature, comprised of about 4500 data points assimilating three influencing input variables, including pseudo-reduced pressure (PPr), pseudo-reduced temperature (TPr), and molecular weight (Mw). The PPr and TPr are obtained by calculating the averages of the sample gas critical pressures and critical temperatures. A complicated nonlinear relationship exists between the three influencing variables and the gas density. The data set was divided into a 70:30 ratio for training and testing the model, respectively. Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were applied to train and test the model. Absolute average percentage error (AAPE), coefficient of determination (R2), and root mean squared error (RMSE) were considered in the error metrics to acquire the best possible model. Levenberg–Marquardt backpropagation algorithm was employed for ANN, while subtractive clustering was used for ANFIS. Results showed that natural gas density can be well correlated with numerous inputs using machine learning tools (ANN and ANFIS). The input parameters include Ppr, Tpr, and Mw, as mentioned above. ANN performed better than ANFIS. The network was adjusted against the training sub-set to set-up weights and biases covering each node. R2 for both testing and training data was more than 99%, while AAPE was around 4% for both cases. Moreover, a detailed mathematical scheme for the ANN model is also provided in this paper.

scholarly journals Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

2020 ◽  
Vol 17 (10) ◽  
pp. 3520 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Laith Abualigah ◽  
Mohamed Abd Elaziz
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Inference System ◽  
Infected People ◽  
Marine Predators ◽  
Forecasting Performance ◽  
The Usa

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

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