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 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 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 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.

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.

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.

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

2019 ◽  
Vol 9 (15) ◽  
pp. 3172 ◽  
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.

scholarly journals The effect of ethanolic leaves extract of Hymenodictyon floribundun on inflammatory biomarkers: a data-driven approach

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Abdullahi Garba Usman ◽  
Mubarak Hussaini Ahmad ◽  
Rabi’u Nuhu Danraka ◽  
Sani Isah Abba
Keyword(s):  
Linear Model ◽  
Fuzzy Inference System ◽  
Molecular Mechanisms ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Inflammatory Biomarkers ◽  
Herbal Products ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Comparison Of The Results

Abstract Background Medicinal plants are used to manage pain and inflammatory disorders in traditional medicine. A scientific investigation could serve as a basis for the determination of molecular mechanisms of antinociceptive and antiinflammatory actions of herbal products. In this work, we used both artificial intelligence (AI) based models inform of adaptive neuro-fuzzy inference system and artificial neural network (ANN) as well as a linear model, namely; stepwise linear regression in modelling the performance of four different inflammatory biomarkers namely; interleukin (1L)-1β, 1L-6, tumour necrosis factor (TNF)-α and prostaglandin E2 (PGE2). This modelling was done using number of abdominal writes, the reaction time of paw licking in mice and paw oedema diameter as the input variables. Results Four different performance indices were employed, which are determination coefficient (DC), root mean squared error (RMSE), mean square error (MSE) and correlation co-efficient (CC). The results have shown the superiority of the AI-based models over the linear model. Conclusions The overall quantitative and visualized comparison of the results showed that adaptive neuro-fuzzy inference system outperformed the ANN and SWLR models in modelling the performance of the four inflammation biomarkers in both the calibration and verification phases.

scholarly journals Hybrid Based Artificial Intellegence Short –Term Load Forecasting

2021 ◽  
pp. 75-87
Author(s):  
Kayode O. Adebunmi ◽  
Temilola M. Adepoju ◽  
Gafari A. Adepoju ◽  
Akeem O. Bisiriyu
Keyword(s):  
Statistical Method ◽  
Fuzzy Inference ◽  
Load Forecasting ◽  
Fuzzy Model ◽  
Absolute Error ◽  
Multilinear Regression ◽  
Power Utility ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Power Load

Electrical power load forecasting, which forms a key element in the power industry's electricity preparation, is used for providing required data for day-to-day system management activities and power utility unit participation. Since the statistical method is a linear model, and the load and meteorological parameters have a nonlinear relationship, the statistical method for load forecasting involves a great calculation time for parameter recognition. Using this tool for load forecasting often results in a major mistake in prediction. Due to the disadvantages of the statistical method of load forecasting Neuro-fuzzy model was used in this work. Three models: Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Multilinear Regression (MLR) were simulated in MATLAB environment and their output results were compared using root mean square error (RMSE) and mean absolute error (MAE). The ANFIS model outperforms the other models with least errors of RMSE and MAE of 2.2198% and 1.7932% 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.

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