Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Gummadi, Jayaram; Bhatt, Deepak L.; Adusumilli, Srujana; Devabhaktuni, Vijay; Acosta, William; Kumar, Ashok

doi:10.1002/ep.11937

Cited by 6 publications

(5 citation statements)

References 38 publications

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“…It has been widely applied in various fields, including environmental science, agriculture, and engineering. For instance, it has been utilized to classify medical images 21 and predict indoor radon concentration 22 .…”

Section: Improved Rf Methods For the Estimation Of Bsfcmentioning

confidence: 99%

Random forest method for estimation of brake specific fuel consumption

Yun,

Wang,

Yao

et al. 2023

Sci Rep

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The internal combustion engine is a widely used power equipment in various fields, and its energy utilization is measured using brake specific fuel consumption (BSFC). BSFC map plays a crucial role in the analysis, optimization, and assessment of internal combustion engines. However, due to cost constraints, some values on the BSFC map are estimated using techniques like K-nearest neighbor, inverse distance weighted interpolation, and multi-layer perceptron, which are recognized for their limited accuracy, particularly when dealing with distributed sampled data. To address this, an improved random forest method is proposed for the estimation of BSFC. Polynomial features are employed to increase higher dimensions of features for random forest by nonlinear transformation, and critical parameters are optimized by particle swarm optimization algorithms. The performance of different methods was compared on two datasets to estimate 20%, 30%, and 40% of BSFC data, and the results reveal that the method proposed in this paper outperforms other common methods and is suitable for estimating the BSFC map.

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Section: Improved Rf Methods For the Estimation Of Bsfcmentioning

confidence: 99%

Random forest method for estimation of brake specific fuel consumption

Yun,

Wang,

Yao

et al. 2023

Sci Rep

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show abstract

“…The major strength of these networks is their ease of implementation and management and therefore, they are suitable for approximating any type of input and output (Hornik et al, 1989).When designing theANN70% of the data were used for training, 15% for testing and 15% for validation. The ANN structure was manually adjusted until the highest correlation was achieved using a trial-and-error process (Gummadi, 2013 andMorgan et al, 2017). The ANNs performance was assessed in terms of the coefficient of determination (r 2 ).…”

Section: Filling the Gaps Of The Ndvi Time Seriesmentioning

confidence: 99%

Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data

Faisal

Makar²

2022

JANS

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The presence of gaps or missing values in time series prevents the practical use of such data. The current research aims at developing a simplified, straightforward technique for gap-filling the time series data of the Normalize Difference Vegetation Index (NDVI) generated using Moderate Resolution Imaging Spectroradiometer (MODIS). This research assumes that a relationship exists between the pixel location, date of acquisition and its NDVI value within a defined timeline. Therefore, two relatively simple methods were tested: the Multiple Linear Regression (MLR) analysis and the Artificial Neural Networks (ANN)to fill the NDVI missing values. While MLR is a well-known simple statistical method, the ANN has been successfully applied for the analysis of various scientific data, including the gap-filling of time series data. Nevertheless, ANN proved its supremacy in such approach. The accuracy of estimation utilizing the developed ANN model reached an average of r2 of 0.8, while the average accuracy of MLR was about 0.3. Nevertheless, the developed model could only be applied within the same timeframe of the images used for developing the model. Otherwise, the accuracy of determination was reduced significantly. The results showed that according to its performance, ANN are promising for filling missing data of NDVI time series and could be applied to any other vegetation indices as well.

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“…Therefore, it can be concluded with high certainty that the model results are valid for any operating conditions constructed by the predefined ranges. Detailed comparison between different statistical approaches e.g., Artificial Neural Network (ANN), Support Vector Regression (SVR) and Kriging can be found in literatures [33,34] .…”

Section: Model Testingmentioning

confidence: 99%

Statistical investigation of a dehumidification system performance using Gaussian process regression

Akhlaghi

Zhao

Shittu

et al. 2019

Energy and Buildings

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Swift performance assessment of dehumidification systems, in design stage and while operation of the system is of substantial importance for commercialization and wide implementation of this technology. This paper presents a novel statistical model, employing Gaussian Process Regression (GPR) to investigate performance of a solar/waste energy driven dehumidification/regeneration cycle with a solid adsorbent bed. The statistical model takes thousands of operating conditions derived from a numerical model to predict the performance of the system. This predictive tool directly correlates the main operating parameters with the performance parameters of the system. The operating parameters considered in this study are: temperature, relative humidity and flow rate of process air, temperature of regeneration air, length of the desiccant bed, solar radiation intensity and operating time, and the selected performance parameters are: moisture extraction efficiency for the dehumidification cycle and moisture removal efficiency for the regeneration cycle. The model is evaluated by three metrics, namely: root mean square error (RSME), mean absolute percentage error (MAPE), and coefficient of determination (R 2). The maximum RSME and MAPE for moisture extraction are only 0.045, 0.21%, and for moisture removal efficiencies are 0.082 and 0.39%, respectively, while the R 2 value is derived as 0.97. The developed model is used to investigate the impact of four selected operating parameters on system performance. Additionally, the system performance is predicted for randomly generated operating conditions as well as warm and humid climates. The developed GPR model provides a swift and highly accurate predictive tool for design of the dehumidification systems and for commercialization of the investigated dehumidification systems.

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Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Cited by 6 publications

References 38 publications

Random forest method for estimation of brake specific fuel consumption

Random forest method for estimation of brake specific fuel consumption

Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data

Statistical investigation of a dehumidification system performance using Gaussian process regression

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