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

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

10.21203/rs.3.rs-83375/v1 ◽

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.

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

Applied Sciences ◽

10.3390/app9183841 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3841 ◽

Cited By ~ 28

Author(s):

Ly ◽

Pham ◽

Dao ◽

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.

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Sea Level Prediction Using Machine Learning

Water ◽

10.3390/w13243566 ◽

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.

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Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

Applied Sciences ◽

10.3390/app9153172 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3172 ◽

Cited By ~ 24

Author(s):

Hoang-Long Nguyen ◽

Thanh-Hai Le ◽

Cao-Thang Pham ◽

Tien-Thinh Le ◽

Lanh Si Ho ◽

...

Keyword(s):

Artificial Intelligence ◽

Support Vector Machine ◽

Mean Squared Error ◽

Fuzzy Inference ◽

Absolute Error ◽

Support Vector ◽

Inference System ◽

Stone Matrix Asphalt ◽

Hybrid Artificial Intelligence ◽

Stone Matrix

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.

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Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature

Materials ◽

10.3390/ma14081983 ◽

2021 ◽

Vol 14 (8) ◽

pp. 1983

Author(s):

Mahmood Ahmad ◽

Ji-Lei Hu ◽

Feezan Ahmad ◽

Xiao-Wei Tang ◽

Maaz Amjad ◽

...

Keyword(s):

Compressive Strength ◽

High Temperature ◽

Supervised Learning ◽

Mean Squared Error ◽

Cement Content ◽

Coefficient Of Determination ◽

Percentage Error ◽

Learning Methods ◽

Inference System ◽

Compressive Strength Of Concrete

Supervised learning algorithms are a recent trend for the prediction of mechanical properties of concrete. This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, based on the experimental data of 207 tests. The cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature were used as inputs for the models’ development. The performance of the AdaBoost, RF, and DT models are assessed using statistical indices, including the coefficient of determination (R2), root mean squared error-observations standard deviation ratio (RSR), mean absolute percentage error, and relative root mean square error. The applications of the above-mentioned approach for predicting the compressive strength of concrete at high temperature are compared with each other, and also to the artificial neural network and adaptive neuro-fuzzy inference system models described in the literature, to demonstrate the suitability of using the supervised learning methods for modeling to predict the compressive strength at high temperature. The results indicated a strong correlation between experimental and predicted values, with R2 above 0.9 and RSR lower than 0.5 during the learning and testing phases for the AdaBoost model. Moreover, the cement content in the mix was revealed as the most sensitive parameter by sensitivity analysis.

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

Journal of Water and Land Development ◽

10.2478/jwld-2019-0014 ◽

2019 ◽

Vol 40 (1) ◽

pp. 127-135 ◽

Cited By ~ 2

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).

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

International Journal of Economics and Finance ◽

10.5539/ijef.v13n7p1 ◽

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.

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

Journal of Hydroinformatics ◽

10.2166/hydro.2016.078 ◽

2016 ◽

Vol 18 (4) ◽

pp. 724-740 ◽

Cited By ~ 1

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.

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Application of Adaptive Network-Based Fuzzy Inference System (ANFIS) for River Stage Prediction in a Tropical Catchment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.735.195 ◽

2015 ◽

Vol 735 ◽

pp. 195-199 ◽

Cited By ~ 4

Author(s):

Nadeem Nawaz ◽

Sobri Harun ◽

Amin Talei

Keyword(s):

Fuzzy Inference System ◽

Hydrological Modeling ◽

Fuzzy Inference ◽

Absolute Error ◽

Coefficient Of Determination ◽

Adaptive Network ◽

Arx Model ◽

Inference System ◽

River Stage ◽

Statistical Measures

Computational intelligence (CI) tools have been successfully applied in different fields with superior performances. Neuro-fuzzy system (NFS) is one the approach which combines the benefits of two powerful CI tools known as artificial neural networks (ANN) and fuzzy logic. Although NFS has attracted researchers in many areas of study, few of its applications have been undertaken in hydrological modeling. Adaptive Network-based Fuzzy Inference System (ANFIS) is so far the most established NFS technique and this study is an application of ANFIS in river stage prediction by using rainfall and stage antecedents as inputs in the tropical catchment of Bekok River in Malaysia. To evaluate the performance of the ANFIS model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The results of this study were evaluated based on several statistical measures such as coefficient of efficiency (CE), coefficient of determination (r2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results showed that ANFIS can successfully predict the river stage and it outperforms ARX model significantly. ANFIS was also found better in estimating peak river stages comparing to ARX model. This study demonstrates the auspicious potential of ANFIS in river stage modeling.

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Comparative Analysis of the Heating Values of Biomass Based on GA-ANFIS and PSO-ANFIS Models

ASME 2019 Power Conference ◽

10.1115/power2019-1825 ◽

2019 ◽

Author(s):

Obafemi Olatunji ◽

Stephen Akinlabi ◽

Nkosinathi Madushele ◽

Paul Adedeji ◽

Samuel Fatoba

Keyword(s):

Mean Squared Error ◽

Fuzzy Inference ◽

Input Parameter ◽

Volatile Matter ◽

Proximate Analysis ◽

Percentage Error ◽

Absolute Deviation ◽

Inference System ◽

Neuro Fuzzy ◽

Wide Range

Abstract This article applied a hybridized, adaptive neuro-fuzzy inference system ANFIS-genetic algorithm (GA-ANFIS) and ANFIS -Particle swarm optimization (PSO-ANFIS) to predict the HHV of biomass. The minimum input parameter for the prediction model is based on the proximate values of biomass which are fixed carbon (FC), ash content (A) and volatile matter (VM). The 214 data which cover a wide range of biomass classes were extracted from reliable literature for the training and testing of the models. The optimal results obtained based on each modelling algorithm were compared. The proposed algorithms were evaluated by statistical indices which are the Coefficient of Correlation (CC), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) estimated at 0.9189, 1.2369,7.4575 and 1.3560 respectively for PSO-ANFIS and 0.9088, 1.1200, 6.3960, 0.8895 respectively for GA-ANFIS. The GA showed exceptional ability to generalize in term of MAPE though at the expense of lesser CC which is obtained in the case of PSO. The reported indices showed that PSO-ANFIS and GA-ANFIS could be applied as an approach to the prediction of HHV based on proximate analysis instead of lengthy experiment procedures.

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