Development of a geogenic radon hazard index—concept, history, experiences
P Bossew, G Cinelli, G Ciotoli, QG Crowley… - International Journal of …, 2020 - mdpi.com
Exposure to indoor radon at home and in workplaces constitutes a serious public health risk
and is the second most prevalent cause of lung cancer after tobacco smoking. Indoor radon …
and is the second most prevalent cause of lung cancer after tobacco smoking. Indoor radon …
Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation
Missing data is a common problem in real-world data sets and it is amongst the most
complex topics in computer science and many other research domains. The common ways …
complex topics in computer science and many other research domains. The common ways …
[HTML][HTML] Mapping the geogenic radon potential for Germany by machine learning
The radioactive gas radon (Rn) is considered as an indoor air pollutant due to its detrimental
effects on human health. In fact, exposure to Rn belongs to the most important causes for …
effects on human health. In fact, exposure to Rn belongs to the most important causes for …
Radiological assessment of indoor radon and thoron concentrations and indoor radon map of dwellings in Mashhad, Iran
M Adelikhah, A Shahrokhi, M Imani… - International Journal of …, 2021 - mdpi.com
A comprehensive study was carried out to measure indoor radon/thoron concentrations in
78 dwellings and soil-gas radon in the city of Mashhad, Iran during two seasons, using two …
78 dwellings and soil-gas radon in the city of Mashhad, Iran during two seasons, using two …
Anomalies prediction in radon time series for earthquake likelihood using machine learning-based ensemble model
The ability to predict the radioactive soil radon gas concentration is important for human
beings because it serves as a precursor to earthquakes. Several studies have been …
beings because it serves as a precursor to earthquakes. Several studies have been …
Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation
To mitigate the greenhouse gas effect, accurate and precise landfill gas prediction models
are required for more precise prediction of the amount and recovery time of methane gas …
are required for more precise prediction of the amount and recovery time of methane gas …
Real-time detection of wildfire risk caused by powerline vegetation faults using advanced machine learning techniques
Wildfires, also known as bushfires, happened more and more frequently in the last decades.
Especially in countries like Australia, the dry and warm climate there make bushfire become …
Especially in countries like Australia, the dry and warm climate there make bushfire become …
Spatial multi-criteria approaches for estimating geogenic radon hazard index
The geogenic radon hazard index (GRHI) map plays a crucial role in evaluating radon
exposure risks. The construction of this map requires a comprehensive analysis of radon …
exposure risks. The construction of this map requires a comprehensive analysis of radon …
Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data
A new methodology, imputation by feature importance (IBFI), is studied that can be applied
to any machine learning method to efficiently fill in any missing or irregularly sampled data. It …
to any machine learning method to efficiently fill in any missing or irregularly sampled data. It …
Spatial modeling of radon potential mapping using deep learning algorithms
Radon potential mapping is challenging due to the limited availability of information. In this
study, a new modeling process using deep learning models based on convolution neural …
study, a new modeling process using deep learning models based on convolution neural …