Evaluation and prediction of the rock static and dynamic parameters

Khosravi, Marzieh; Tabasi, Somayeh; Eldien, Hany Hossam; Motahari, Mohammad Reza; Alizadeh, Sayed Mehdi

doi:10.1016/j.jappgeo.2022.104581

Cited by 31 publications

(19 citation statements)

References 41 publications

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“…Multivariate exploratory data analysis (EDA) is performed to understand the internal distribution of the attributes of the variables [62]. Temporal distribution of all the variables is explored using several visual and numerical representation.…”

Section: Multivariate Exploratory Data Analysismentioning

confidence: 99%

“…In some countries, solar energy uses a significant percentage of the sun's energy and has a more predictable behavior than wind-based energy. 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].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: Multivariate Exploratory Data Analysismentioning

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

“…Support Vector Machine basically considers the data points that are within the decision boundary line and the hyperplane that is capable of including a maximum number of data points can be selected as the best-fitted line [61]. For each training point 𝑥 𝑖 (𝑖 ≤ 𝑛) (𝑛 = total number of data points).…”

Section: Machine Learning Regressorsmentioning

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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“…In this research, a neural network with one LSTM hidden unit accompanied by a dense layer connecting the LSTM target output at the last time-step (t-1) to a single output neuron with non-linear activation function. The LSTM model was trained using the deep learning library, Keras in Python, the ReLU activation function, and the RMSE, MAE, and R 2 function [47][48][49][50]. To predict the discharge variable of a time-step in the future e.g., daily/weekly, values of the variables at the previous time-steps are used.…”

Section: Long Short-term Memory (Lstm) Recurrent Neuralmentioning

confidence: 99%

Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms

Akatu¹,

Khosravi²,

Mehedi³

et al. 2022

Preprint

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The dynamics of suspended sediment involves inherent non-linearity and complexity as a result of the presence of both spatial variability of the basin characteristics and temporal climatic patterns. As a result of this complexity, the conventional sediment rating curve (SRC) and other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged as one of the advanced modeling techniques capable of addressing inherent non-linearity in hydrological processes over the last few decades. DNN algorithms are used to perform predictive analysis and investigate the interdependencies among the most pivotal water quantity and quality parameters i.e., discharge, suspended sediment concentration (SSC), and turbidity. In this study, the Long short-term memory (LSTM) algorithm of DNNs is used to model the discharge-suspended sediment relationship for the Stony Clove Creek. The simulations were run using primary data on discharge, SSC and turbidity. For the development of the DNN models and examining the effects of input vectors, combinations of different input vectors (namely discharge, and SSC) for the current and previous days are considered. Furthermore, a suitable modelling approach with an appropriate model input structure is suggested based on model performance indices for the training and testing phases. The performance of developed models is assessed using statistical indices such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Statistically, the performance of DNN-based models in simulating the daily SSC performed well with observed sediment concentration series data. The study demonstrates the suitability of the DNN approach for simulation and estimation of daily SSC, opening up new research avenues for applying hybrid soft computing models in hydrology.

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Evaluation and prediction of the rock static and dynamic parameters

Cited by 31 publications

References 41 publications

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

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

Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms

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