Quantifying Small-Scale Hyporheic Streamlines and Resident Time under Gravel-Sand Streambed Using a Coupled HEC-RAS and MIN3P Model

Mehedi, Md Abdullah Al; Yazdan, Munshi Md. Shafwat; Ahad, Tanvir; Akatu, Wisdom; Kumar, Raaghul; Rahman, Ashiqur

doi:10.3390/eng3020021

Cited by 22 publications

(14 citation statements)

References 53 publications

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“…Additionally, the use of renewable energy spurs economic growth on a local, regional, and international scale and generates new job possibilities [11][12][13][14]. Utilizing renewable energy has the advantage of lessening environmental degradation [15][16][17][18]. Solar energy has emerged as one of the most important sources of energy in recent years [19,20].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Forecasting Energy Consumption Time Series Using Recurrent Neural Network in Tensorflow

Yazdan¹,

Khosravi²,

Saki³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Among all the various forecasting algorithms in ML, Linear Regression (LR) model is one of the common ML algorithms which also includes Ridge Regressions (RR) and Lasso Regressions (LaR) [10,11]. Other commonly used ML approaches are Decision Tree (DT) [12][13][14], Random Forest (RF) [15][16][17], Support Vector Machine (SVM) [18][19][20][21][22], Gradient Boosting Machine (GBM) [23][24][25][26], K Nearest Neighbors (KNN) [27][28][29][30], and Artificial Neural Network (ANN) [31][32][33][34]. Recently, Successful ML models using out-of-sample data set over predicted housing pricing, including support vector regression, regression tree, random forecast, bagging, boosting, Ridge and Lasso, and ensemble learning, have been discovered to be more efficient and realistic [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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

Khosravi¹,

Arif²,

Ghaseminejad³

et al. 2022

Preprint

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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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“…The capability of numerical simulation of flow as a tool for investigating science in general and hydrogeologic sciences, in particular, has reached a new level in recent days [1,2]. The direction of this type of research represents a promising future aimed at providing useful alternatives to laboratory experiments where realistic input parameters into hydrogeologic simulations are strenuous [3][4][5][6]. Water flow mechanisms in the subsurface media are subject to many fields [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Automated Particle Tracing & Sensitivity Analysis for Residence Time in a Saturated Subsurface Media

Mehedi

Yazdan

2022

Liquids

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Residence time of water flow is an important factor in subsurface media to determine the fate of environmental toxins and the metabolic rates in the ecotone between the surface stream and groundwater. Both numerical and lab-based experimentation can be used to estimate the residence time. However, due to high variability in material composition in subsurface media, a pragmatic model set up in the laboratory to trace particles is strenuous. Nevertheless, the selection and inclusion of input parameters, execution of the simulation, and generation of results as well as post-processing of the outcomes of a simulation take a considerable amount of time. To address these challenges, an automated particle tracing method is developed where the numerical model, i.e., flow and reactive transport code, MIN3P, and MATLAB code for tracing particles in saturated porous media, is used. A rectangular model domain is set up considering a fully saturated subsurface media under steady-state conditions in MIN3P. Streamlines and residence times of the particles are computed with a variety of seeding locations covering the whole model surface. Sensitivity analysis for residence time is performed over the varying spatial discretization and computational time steps. Moreover, a comparative study of the outcomes with Paraview is undertaken to validate the automated model (R2 = 0.997). The outcome of the automated process illustrates that the computed residence times are highly dependent on the accuracy of the integration method, the value of the computational time step, ∆t, spatial discretization, stopping criterion for the integration process of streamlines, location, and amount of seed points. The automated process can be highly beneficial in obtaining insights into subsurface flow dynamics with high variability in the model setup instead of laboratory-based experimentation in a computationally efficient manner.

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Quantifying Small-Scale Hyporheic Streamlines and Resident Time under Gravel-Sand Streambed Using a Coupled HEC-RAS and MIN3P Model

Cited by 22 publications

References 53 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

Automated Particle Tracing & Sensitivity Analysis for Residence Time in a Saturated Subsurface Media

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