Bagged neural network model for prediction of the mean indoor radon concentration in the municipalities in Czech Republic

2017 ◽  
Vol 166 ◽  
pp. 398-402 ◽  
Author(s):  
Jana Timkova ◽  
Ivana Fojtikova ◽  
Petra Pacherova
Keyword(s):  
Neural Network ◽  
Czech Republic ◽  
Network Model ◽  
Neural Network Model ◽  
Radon Concentration ◽  
Indoor Radon ◽  
Indoor Radon Concentration ◽  
The Mean

scholarly journals Assessing the Impact of Housing Features and Environmental Factors on Home Indoor Radon Concentration Levels on the Navajo Nation

2020 ◽  
Vol 17 (8) ◽  
pp. 2813
Author(s):  
Sheldwin A. Yazzie ◽  
Scott Davis ◽  
Noah Seixas ◽  
Michael G. Yost
Keyword(s):  
Radon Concentration ◽  
Indoor Radon ◽  
Decay Product ◽  
Indoor Radon Concentration ◽  
Navajo Nation ◽  
Home Ventilation ◽  
Radon Gas ◽  
The Mean ◽  
The Impact ◽  
Concentration Levels

Uranium is naturally found in the environment as a radioactive metal element with high concentrations in the Southwestern US. In this region is the Navajo Nation, which spans approximately 69,930 square kilometers. A decay product of uranium is radon gas, a lung carcinogen that has no color, odor, or taste. Radon gas may pass from soil into homes; and, indoor accumulation has been associated with geographical location, seasonality, home construction materials, and home ventilation. A home and indoor radon survey was conducted from November 2014 through May 2015, with volunteers who reported residence on the Navajo Nation. Home geolocation, structural characteristics, temperature (°C) during radon testing, and elevation (meters) were recorded. Short-term indoor radon kits were used to measure indoor radon levels. 51 homes were measured for indoor radon levels, with an arithmetic mean concentration of 60.5 Becquerels per cubic meter (Bq/m3) (SD = 42.7). The mean indoor radon concentrations (Bq/m3) by house type were: mobile, 29.0 (SD = 22.9); wood, 58.6 (SD = 36.0); hogan, 74.0 (SD = 0.0); homes constructed of cement and wood, 82.6 (SD = 3.5); and homes constructed of concrete and cement, 105.7 (SD = 55.8). A key observation is that house construction type appears to be associated with the mean home indoor radon concentration. This observation has been published in that the basic structural make-up of the home may affect home ventilation and therefore indoor radon concentration levels.

scholarly journals Computational Fluid Dynamic Design and Experimental Study of a Temperature Sensor Array Used in Climate Reference Station

2019 ◽  
Vol 36 (9) ◽  
pp. 1835-1847
Author(s):  
Jie Yang ◽  
Qingquan Liu ◽  
Wei Dai
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Air Temperature ◽  
Neural Network Model ◽  
Temperature Sensor ◽  
Sensor Array ◽  
The Neural Network ◽  
Temperature Observation ◽  
The Mean ◽  
Cfd Method

Accurate air temperature measurements are demanded for climate change research. However, air temperature sensors installed in a screen or a radiation shield have traditionally resisted observation accuracy due to a number of factors, particularly solar radiation. Here we present a novel temperature sensor array to improve the air temperature observation accuracy. To obtain an optimum design of the sensor array, we perform a series of analyses of the sensor array with various structures based on a computational fluid dynamics (CFD) method. Then the CFD method is applied to obtain quantitative radiation errors of the optimum temperature sensor array. For further improving the measurement accuracy of the sensor array, an artificial neural network model is developed to learn the relationship between the radiation error and environment variables. To assess the extent to which the actual performance adheres to the theoretical CFD model and the neural network model, air temperature observation experiments are conducted. An aspirated temperature measurement platform with a forced airflow rate up to 20 m s−1 served as an air temperature reference. The average radiation errors of a temperature sensor equipped with a naturally ventilated radiation shield and a temperature sensor installed in a screen are 0.42° and 0.23°C, respectively. By contrast, the mean radiation error of the temperature sensor array is approximately 0.03°C. The mean absolute error (MAE) between the radiation errors provided by the experiments and the radiation errors given by the neural network model is 0.007°C, and the root-mean-square error (RMSE) is 0.009°C.

scholarly journals Numerical Simulation of Wave Overtopping on Breakwater with an Armor Layer of Accropode Using SWASH Model

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 386 ◽  
Author(s):  
Na Zhang ◽  
Qinghe Zhang ◽  
Keh-Han Wang ◽  
Guoliang Zou ◽  
Xuelian Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Error Rate ◽  
Empirical Formula ◽  
Physical Model Test ◽  
Roughness Coefficient ◽  
Wave Overtopping ◽  
Mean Error ◽  
The Mean

In this paper, a new method for predicting wave overtopping discharges of Accropode armored breakwaters using the non-hydrostatic wave model Simulating WAves till SHore (SWASH) is presented. The apparent friction coefficient concept is proposed to allow the bottom shear stress term calculated in the momentum equation to reasonably represent the effect of comprehensive energy dissipation caused by the roughness and seepage during the wave overtopping process. A large number of wave overtopping cases are simulated with a calibrated SWASH model to determine the values of equivalent roughness coefficients so that the apparent friction coefficients can be estimated to achieve the conditions with good agreement between numerical overtopping discharges and those from the EurOtop neural network model. The relative crest freeboard and the wave steepness are found to be the two main factors affecting the equivalent roughness coefficient. A derived empirical formula for the estimation of an equivalent roughness coefficient is presented. The simulated overtopping discharges by the SWASH model using the values of the equivalent roughness coefficient estimated from the empirical formula are compared with the physical model test results. It is found that the mean error rate from the present model predictions is 0.24, which is slightly better than the mean error rate of 0.26 from the EurOtop neural network model.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF POSITIVE DYNAMICS FOR TREATMENT OF PATIENTS WITH TUBERCULOSIS

2020 ◽  
Author(s):  
Marina Ermolickaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square Error ◽  
Network Model ◽  
Neural Network Model ◽  
Test Sample ◽  
Mean Square ◽  
Tuberculosis Patients ◽  
The Mean

Using the RStudio program, a neural network model has been developed that predicts positive dynamics in the treatment of tuberculosis patients in a tuberculosis dispensary hospital. The accuracy of the presented model on the test sample is 99.4%, the mean square error (MSE) is 0.013.

Assessment of Annual Tracheobronchial Effective Dose from Indoor Radon Inhalation in Selected Residential Buildings in Southwestern Nigeria

2021 ◽  
Vol 11 (6) ◽  
pp. 79-88
Author(s):  
Olukunle Olaonipekun Oladapo ◽  
Olatunde Micheal Oni ◽  
Emmanuel Abiodun Oni
Keyword(s):  
Effective Dose ◽  
Radon Concentration ◽  
Residential Buildings ◽  
Indoor Radon ◽  
World Health ◽  
Reference Level ◽  
Indoor Radon Concentration ◽  
Effective Doses ◽  
The Mean ◽  
Radon Inhalation

Background and Purpose: Radon-222 is a major human health challenge among all sources of ionizing radiation. For most people, the greatest exposure to radon comes from homes and affects mainly the respiratory tract, especially the tracheobronchial region. This work assesses the annual tracheobronchial effective dose from indoor radon inhalation in residential buildings with different covering materials for walls, ceilings and floor using different dosimetric lung models. Method: A total of 180 residential buildings with commonest combination of covering materials in some cities in South-western Nigeria were investigated using an active electronic radon gas detector, RAD 7. The commonest combination of covering materials were (A): paint, paint, carpet; (B): paint fiber board, plastic tiles; (C): paint, fiber board, ceramic tiles for walls, ceilings and floors respectively. Result: The mean indoor radon concentration measured ranged between 23.08 Bq m-3 and 72.14 Bq m-3 for all the residential buildings investigated. Buildings with covering materials C, presented the highest radon concentration. Generally, the mean indoor radon concentration for all combinations of covering materials in all the cities investigated were found to be lower than the recommended action level of 200 Bqm-3 and the reference level of 100 Bqm-3 set by International Commission on for Radiation Protection and World Health Organization respectively. The annual tracheobronchial effective dose estimated for the different lung dose models ranged from 0.91 mSv – 3.27 mSv for combination (A), 1.00 mSv - 3.60 mSv for combination (B) and 1.09 mSv – 3.94 mSv for combination (C). It revealed that the more recent model gives greater value of the annual tracheobronchial effective dose. It was observed that only the annual tracheobronchial effective doses obtained by the James model presented values that are within the recommended ICRP intervention level of (3-10) mSvy-1. Other models gave values of annual tracheobronchial effective doses below the ICRP recommended intervention levels. Conclusion: These imply that all the residential buildings and the different combination of covering materials surveyed in this work will not pose any radiological hazard to the inhabitants. Key words: Indoor Radon Inhalation, Radon-222, annual tracheobronchial effective dose, residential buildings

Sign in / Sign up

Export Citation Format

Share Document