Application of machine learning methods for estimating and comparing the sulfur dioxide absorption capacity of a variety of deep eutectic solvents

Zhu, Xiaolei; Khosravi, Marzieh; Vaferi, Behzad; Amar, Menad Nait; Ghriga, Mohammed Abdelfetah; Mohammed, Adil Hussein

doi:10.1016/j.jclepro.2022.132465

Cited by 42 publications

(20 citation statements)

References 84 publications

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“…The performance of the RNN neural network is evaluated using three error matrices e.g., Root Mean Square Error (RMSE), the coefficient of determination (R 2 ) and Nash Sutcliffe model efficiency coefficient (E) [63]. Further, the performance of the model also evaluated and improved through increasing the number of iterations i.e., epoch in the neural network.…”

Section: Model Evaluation Matrices and Improvementmentioning

confidence: 99%

“…As a result, it ranks among the most significant renewable energy sources for a variety of nations in south Europe, including Spain, as well as other places along the same latitude, such as Saudi Arabia or India [21][22][23]. Thermal solar energy, which transforms solar radiation into thermal energy used to heat buildings, desalination plants, homes, and water treatment facilities, among other things, and photovoltaic solar energy, which transforms solar radiation into electrical energy that can be transported for purposes other than heating [24][25][26]. A plentiful natural resource and sustainable energy source is wind.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Yazdan¹,

Khosravi²,

Saki³

et al. 2022

Preprint

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The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

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Section: Model Evaluation Matrices and Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Yazdan¹,

Khosravi²,

Saki³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The closer the correlation coefficient is to 0, the weaker the correlation. By calculating the Pearson correlation coefficient of the feature variable by formula (1) [18], it can be judged whether the selected feature is reasonable. If the absolute value of the correlation coefficient between variables is greater than 0.75, there may be a multicollinearity problem, indicating that the feature selection is unreasonable [19].…”

Section: Feature Correlation Analysis Based On Pearsonmentioning

confidence: 99%

Dynamic Fire Risk Classification Prediction of Stadiums: Multi-Dimensional Machine Learning Analysis Based on Intelligent Perception

Fan

Zhao

et al. 2022

Applied Sciences

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Stadium fires can easily cause massive casualties and property damage. The early risk prediction of stadiums will be able to reduce the incidence of fires by making corresponding fire safety management and decision making in an early and targeted manner. In the field of building fires, some studies apply data mining techniques and machine learning algorithms to the collected risk hazard data for fire risk prediction. However, most of these studies use all attributes in the dataset, which may degrade the performance of predictive models due to data redundancy. Furthermore, machine learning algorithms are numerous and applied to fewer stadium fires, and it is crucial to explore models suitable for predicting stadium fire risk. The purpose of this study was to identify salient features to build a model for predicting stadium fire risk predictions. In this study, we designed an index attribute threshold interval to classify and quantify different fire risk data. We then used Gradient Boosting-Recursive Feature Elimination (GB-RFE) and Pearson correlation analysis to perform efficient feature selection on risk feature attributes to find the most informative salient feature subsets. Two cross-validation strategies were employed to address the dataset imbalance problem. Using the smart stadium fire risk data set provided by the Wuhan Emergency Rescue Detachment, the optimal prediction model was obtained based on the identified significant features and six machine learning methods of 12 combination forms, and full features were input as an experimental comparison study. Five performance evaluation metrics were used to evaluate and compare the combined models. Results show that the best performing model had an F1 score of 81.9% and an accuracy of 93.2%. Meanwhile, by introducing a precision-recall curve to explain the actual classification performance of each model, AdaBoost achieves the highest Auprc score (0.78), followed by SVM (0.77), which reveals more stable performance under such imbalanced data.

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“…Park and Bae [43,44] develop a housing price forecasting model based on AI and ML algorithms such as Naïve Bayesian, and AdaBoost while comparing them to the classification accuracy performance. Ho et al [45] compared three ML methods of SVM, RF, and GBM on housing prices over a period of 18 years considering three error metrics of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) [46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

Khosravi¹,

Arif²,

Ghaseminejad³

et al. 2022

Preprint

Self Cite

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Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.

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Application of machine learning methods for estimating and comparing the sulfur dioxide absorption capacity of a variety of deep eutectic solvents

Cited by 42 publications

References 84 publications

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Dynamic Fire Risk Classification Prediction of Stadiums: Multi-Dimensional Machine Learning Analysis Based on Intelligent Perception

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

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