Artificial Neural Network Optimization to Estimate Radon in Soil

Soil gas radon and indoor radon measurements have been carried out in Mayo-Louti and Benoué Divisions in northern Cameroon. Concentrations of radon in soil have been measured using Markus 10 at the depth of about 1 m. Radon concentration in soil varies from 0.9 to 13.8 kBq m−3 with a mean value of 4.6 kBq m−3. Average daily indoor radon concentrations measured with RadonEye+2 detectors vary from 7 to 60 Bq m−3 with an average of 17 Bq m−3. Indoor radon concentrations measured with passive RADTRAK detectors range between 15 and 104 Bq m−3 with a geometric value of 38 Bq m−3 and a geometric standard deviation of 1.5. This geometric value is lower than the value of 30 Bq m–3 given by UNSCEAR. Indoor radon inhalation dose ranges between 0.28 and 1.97 mSv a−1 with geometric value of 0.72 mSv a−1 (at 0.03 standard deviation). Outdoor radon inhalation ranges between 0.02 and 0.26 mSv a−1 with a mean value of 0.09 mSv a−1. The total annual effective dose due to indoor and outdoor radon exposure for this study area is 0.81 mSv a−1, less than 1.15 mSv a−1 the world average value given by UNSCEAR. There is no significant radiological risk for the inhabitants.

IntroductionData on outdoor radon are generally scarce compared to indoor radon. However, knowledge of the spatial distribution of outdoor radon is necessary to estimate the overall exposure of the population to radon, it supports the prediction of indoor radon and characterizes the natural radon background. Germany has a comprehensive dataset on long-term outdoor radon concentration and the equilibrium factor at national level, which allowed to produce what is probably the only spatially continuous outdoor radon map at national level so far. DataIn this study, outdoor radon concentration measurement data (n = 172) and equilibrium factors (n = 25) from a national survey from 2003 to 2006 were reanalyzed using state-of-the-art machine learning routines. Spatially comprehensive maps of distance to the sea, radon concentration in soil, sand content in topsoil and a terrain-based wind exposure index are used as predictors. MethodsQuantile regression forest was used to map the conditional distribution of outdoor radon concentration at 500 m grid resolution. The equilibrium factor was mapped using a linear regression model. Both maps were combined to derive the equivalent outdoor radon equilibrium concentration. Population weighting of the results was achieved by explicitly accounting for the population distribution using a probabilistic sampling procedure from the estimated conditional distributions. ResultsThe arithmetic mean and the interquartile range (25th to 75th percentile) for the population-weighted outdoor radon concentration for Germany are 9.3 Bq/m³ and 5.8 Bq/m³ to 11.2 Bq/m³, respectively. The mean equilibrium factor is 0.49. The arithmetic mean and the interquartile range (25th to 75th percentile) for the population-weighted outdoor radon equilibrium equivalent concentration are 4.7 Bq/m³ and 2.7 Bq/m³ to 5.9 Bq/m³ respectively. The estimated inhalation dose due to outdoor exposure to radon is 0.056 mSv/a (arithmetic mean), with less than 10 % of the population exceeding a value of 0.1 mSv/a. The unavoidable inhalation dose due to radon exposure (outdoors plus indoors) in Germany is estimated at an arithmetic mean of 0.37 mSv/a. The spatial distribution of radon outdoors is mainly determined by the distance to the sea. The predictors radon concentration in soil, sand in topsoil and wind exposure still have a significant influence, especially at local to regional level. ConclusionKnowledge about the spatial distribution of outdoor radon and its local variability for Germany was improved using a modern regression technique and relevant predictive information. The results confirm a low outdoor radon concentration with a small contribution to the effective dose received by the population from outdoor radon exposure.

Background: Radon, a radioactive gas, is ubiquitous and commonly inhaled by individuals. It was originating from the Earth's crust. Radon can also be released by building materials, water, basement air, soil, and other environmental components. When radon gas decays, it produces radioactive particles that can be inhaled. These particles damage lung tissue, increasing the risk of lung cancer over time.Materials and Methods: Indoor and outdoor radon concentrations were determined in 24 houses in two cities in Al-Najaf province, Al-Najaf city and Al-Kufa city, using Airthings Corentium Digital Radon Detector.Results and Discussion: The arithmetic mean of indoor radon concentration was 18.09±9.41 Bq/m<sup>3</sup>, while the arithmetic mean of outdoor radon concentration was 4.50±2.96 Bq/m<sup>3</sup>. The arithmetic mean of ‘the annual effective dose’ received by home occupants by indoor radon was 0.46±0.24 mSv/yr. The arithmetic mean of the ‘effective dose to the lung’ was 1.09±0.57 mSv/yr.Conclusion: The total annual effective dose due to indoor and outdoor radon concentration was lower than the reference level of International Commission on Radiological Protection. The results of the radiological survey due to indoor and outdoor radon levels in studied dwellings suggest that the radionuclides and their radiological hazard indexes in all studied dwellings do not impose a health hazard.

In this study, an artificial neural network (ANN) model is developed for the purpose of estimating the output current ripple of a power factor correction (PFC) AC/DC interleaved boost converter (IBC) used in battery charger of electrical vehicles (EVs) based on the inductance current ripple, switching frequency and load changes. Besides, the improved ANN model is compared with some different machine learning (ML) techniques like linear regression (LR), random forest (RF). The PFC-IBC is simulated with the PSIM simulation program to estimate the output current ripple. As a result, 336 output current ripple values are obtained based on inductance current ripple, different switching frequency and load changes. Then, the value of output current ripple is estimated by training the input parameters with LR, RF and ANN machine learning techniques (MLTs) for controlling the current harmonics drawn from the grid and for reliable charging of batteries. It is seen that the estimation value obtained with MLTs is quite compatible with the actual value obtained with the simulation. In addition, in the study carried out with the simulation, it takes a period of several days to obtain the estimation results; whereas, the operation of estimation with MLTs can be completed in a short period such as a few minutes. This clearly reveals the advantage of the MLTs. Therefore, this value is estimated through the MLTs with a high accuracy before the design of the charging device in order to maintain at a secure level the output current ripple posing considerable importance in electrical vehicle battery charge. Also, in this estimation process, LR, RF and developed ANN techniques are examined and compared separately in the WEKA program and it is observed that the developed ANN model proposes better results than other techniques.

Surveys of concentration of radon isotopes in indoor and outdoor air in Japan

Frequency and intensity of the harmful algal blooms (HABs) increased globally since 1970s. The increase in HABs have negatively affected aquatic ecosystem and aquaculture industry. The economic losses were about $ 1 billion in Europe, $ 100 million in USA and $ 121 billion in Korea per year. There were various field monitoring campaigns for ecological and biological researches. However, traditional HABs monitoring has limitations on both spatial and temporal coverage. In these days, multispectral remote sensing methods using satellite sensors have been widely used to monitor HABs in ocean and coastal areas. However, the satellite systems used in ocean and coastal research, such as MODIS, SeaWiFS and etc. have limitations in study on complex coastline, because of their coarse spatial resolution (~ few km). In this research, we conducted two-year intensive monitoring on the South Sea of Korea from 2016 to 2017 at 62 sampling station and used landsat-8 operational land imager (OLI) satellite that has 30m spatial resolution. We used 4 band (band 1 to 4), 4-band ratio (band 1 over band 3 and 4, and band 2 over band 3 and 4) and mixed dataset of 4 band and 4-band ratio. The empirical OC algorithms showed poor performances, under 0.25 of r-squared. The machine learning techniques, i.e., artificial neural network (ANN) and support vector machine (SVM) were applied to enhance performance of estimating chl-a on landsat-8 application. Parameters for developing ANN and SVM model were optimized using a pattern search algorithm in MATLAB toolbox. All dataset were divided into 80 % of training and 20 % of validation data. In the training step, mixed dataset showed the best performance in both ANN and SVM models, whereas 4-band ratio and 4 band dataset in the validation step showed the best performance in ANN and SVM, respectively. The ANN model showed poor performance in low chl-a concentrations but SVM had more accurate performance in low and mid concentrations. Both models under-estimated chl-a in mid to high concentration range. For the mapping results, the ANN model using 4 band dataset showed very low concentration of chl-a in most of research area, whereas SVM showed high concentration of chl-a in coastal area and bay. The result using 4-band ratio dataset showed similar chl-a distribution in ANN and SVM. For mixed dataset results the ANN model estimated over 8 mg m-3 of chl-a at some of coastal, almost zero in near coastal area and over 2 mg m-3 chl-a concentration for off-shore area. In case of SVM, all region showed approximately 2 mg m-3 of chl-a concentration. Landsat-8 OLI was not proper system for OC algorithms. Machine learning techniques were effective tools for enhancing ocean chl-a estimation performance using landsat-8 OLI. Thus, this study showed potential of landsat-8 OLI application to coastal HAB monitoring.

Uranium and its daughters including Ra-226 are naturally present in the Earth's crust and other environmental bodies. During decay of Ra-226 radioactive noble gas radon is produced. This gas emanates to the atmosphere from solid matrixes containing Ra-226. It causes a special problem connected with the fact that radon accumulates in the closed spaces of buildings. Increased concentrations of radon indoors in many cases are the significant source of human exposure to ionizing radiation. Radon daughters having been deposited in the airways of human lungs are the source of alpha particles which irradiates the inner surface of airways. Since radiation quality of alpha radiation is high and small volumes of tissues are being irradiated, the influence of indoor radon as a source of ionizing radiation is significant. In order to forecast indoor radon concentrations and to take necessary remedial (in existing buildings) or prevention (in new buildings) measures, the main sources of indoor radon should be known in each country or geographical region. It may be soil, building materials, water and natural gas. It has been determined that the main source of indoor radon in Lithuania is soil. Permanent investigations of radionuclide content of building materials used or manufactured in Lithuania have not revealed any building materials with concentrations of naturally occurring radionuclides exceeding maximum permitted levels determined by the Lithuanian Hygienic Standards HN 40-1994. These investigations are performed by means of gamma spectrometry using the Ge spectrometer by Oxford after sample grinding and drying. A short review of radon risk mapping techniques used in Sweden, USA, Germany and Czech Republic is presented in paper. These techniques may be used for creation of similar technique in Lithuania with corrections connected with local geology. When determining radon risk mainly two parameters should be taken into account: radium content in soil (or radon content in soil air) which is associated with the type of soil and permeability of soil. The Lithuanian system of radon risk determination is not created yet because more detailed data on radon concentrations in soil air should be collected. Data from field measurements of radon concentrations in soil air and concentrations of naturally occurring radionuclides are presented. These measurements were carried out in some potentially important from the point of view of radon risk regions of Lithuania. Concentrations of Ra-226, Th-228 and K-40 in soil have been measured by gamma spectrometer GR-256 by Exploranium on the surface layer (up to 30 cm) of soil. Concentrations of radon in soil have been measured by MARKUS 10 in the depth of 70 cm. The measurements have been performed directly without sampling and sample preparation by digging the detector of Exploranium and pumping rod of MARKUS 10 in the investigated soil. The results indicate that there are some regions in Lithuania with radon concentrations in soil air exceeding 100 kBq/m3. Though radon risk depends on soil permeability these results show that these areas may be identified as areas of medium or even high radon risk. The system for classification of building sites in terms of indoor radon risk should be created in Lithuania in order to follow requirements of Lithuanian radiation protection standards and to keep below determined action levels of indoor radon- 400 Bq/m3 in existing buildings and 200 Bq/m3 in constructed ones. Results of indoor radon measurements are presented as well. The measurements have been performed in 400 randomly selected detached houses during heating season in two lowest permanently used rooms. Duration of one measurement exceeds 3 weeks. E-PERM electrets have been used for this type of measurements. The results show that the average concentration of indoor radon in Lithuania is 55 Bq/m3. In some cases these concentrations exceed the above-mentioned action levels and approach 2000 Bq/m. It shows that indoor radon problems exist in Lithuania as in many other countries. The average concentration of indoor radon in karst region is 125 Bq/m3. It shows that special attention should be paid to such regions because conditions for increased intake of radon to buildings may exist. Indoor radon is one of the main sources of exposure in Lithuania. In some cases it may be the essential source causing tens of milisieverts of annual effective dose. It shows that the problem of indoor radon is important in Lithuania.

Summary Uranium and its daughters including Ra-226 are naturally present in the Earth's crust and other environmental bodies. During decay of Ra-226 radioactive noble gas radon is produced. This gas emanates to the atmosphere from solid matrixes containing Ra-226. It causes a special problem connected with the fact that radon accumulates in the closed spaces of buildings. Increased concentrations of radon indoors in many cases are the significant source of human exposure to ionizing radiation. Radon daughters having been deposited in the airways of human lungs are the source of alpha particles which irradiates the inner surface of airways. Since radiation quality of alpha radiation is high and small volumes of tissues are being irradiated, the influence of indoor radon as a source of ionizing radiation is significant. In order to forecast indoor radon concentrations and to take necessary remedial (in existing buildings) or prevention (in new buildings) measures, the main sources of indoor radon should be known in each country or geographical region. It may be soil, building materials, water and natural gas. It has been determined that the main source of indoor radon in Lithuania is soil. Permanent investigations of radionuclide content of building materials used or manufactured in Lithuania have not revealed any building materials with concentrations of naturally occurring radionuclides exceeding maximum permitted levels determined by the Lithuanian Hygienic Standards HN 40-1994. These investigations are performed by means of gamma spectrometry using the Ge spectrometer by Oxford after sample grinding and drying. A short review of radon risk mapping techniques used in Sweden, USA, Germany and Czech Republic is presented in paper. These techniques may be used for creation of similar technique in Lithuania with corrections connected with local geology. When determining radon risk mainly two parameters should be taken into account: radium content in soil (or radon content in soil air) which is associated with the type of soil and permeability of soil. The Lithuanian system of radon risk determination is not created yet because more detailed data on radon concentrations in soil air should be collected. Data from field measurements of radon concentrations in soil air and concentrations of naturally occurring radionuclides are presented. These measurements were carried out in some potentially important from the point of view of radon risk regions of Lithuania. Concentrations of Ra-226, Th-228 and K-40 in soil have been measured by gamma spectrometer GR-256 by Exploranium on the surface layer (up to 30 cm) of soil. Concentrations of radon in soil have been measured by MARKUS 10 in the depth of 70 cm. The measurements have been performed directly without sampling and sample preparation by digging the detector of Exploranium and pumping rod of MARKUS 10 in the investigated soil. The results indicate that there are some regions in Lithuania with radon concentrations in soil air exceeding 100 kBq/m3. Though radon risk depends on soil permeability these results show that these areas may be identified as areas of medium or even high radon risk. The system for classification of building sites in terms of indoor radon risk should be created in Lithuania in order to follow requirements of Lithuanian radiation protection standards and to keep below determined action levels of indoor radon- 400 Bq/m3 in existing buildings and 200 Bq/m3 in constructed ones. Results of indoor radon measurements are presented as well. The measurements have been performed in 400 randomly selected detached houses during heating season in two lowest permanently used rooms. Duration of one measurement exceeds 3 weeks. E-PERM electrets have been used for this type of measurements. The results show that the average concentration of indoor radon in Lithuania is 55 Bq/m3. In some cases these concentrations exceed the above-mentioned action levels and approach 2000 Bq/m. It shows that indoor radon problems exist in Lithuania as in many other countries. The average concentration of indoor radon in karst region is 125 Bq/m3. It shows that special attention should be paid to such regions because conditions for increased intake of radon to buildings may exist. Indoor radon is one of the main sources of exposure in Lithuania. In some cases it may be the essential source causing tens of milisieverts of annual effective dose. It shows that the problem of indoor radon is important in Lithuania.

Agricultural sustainability is dependent on the ability to predict crop yield, which is vital for farmers, consumers, and researchers. Most of the works used the amount of rainfall, average monthly temperature, relative humidity, etc. as inputs. In this paper, an attempt was made to predict the yield of the citrus crop (Washington Navel orange, Valencia orange, Murcott mandarin, Fremont mandarin, and Bearss Seedless lime) using weather factors and the accumulated heat units. These variables were used as input parameters in an artificial neural network (ANN) model. The necessary information was gathered during the growing seasons between 2010/2011 and 2021/2022 under Egyptian conditions. Weather factors were daily precipitation, yearly average air temperature, and yearly average of air relative humidity. A base air temperature of 13.0 °C was used to determine the accumulated heat units. The heat use efficiency (HUE) for cultivars was determined. The Bearss Seedless lime had the lowest HUE of 9.5 kg/ha °C day, while the Washington Navel orange had the highest HUE of 20.2 kg/ha °C day. The predictive performance of the ANN model with a structure of 9-20-1 with the backpropagation was evaluated using standard statistical measures. The actual and estimated yields from the ANN model were compared using a testing dataset, resulting in a value of RMSE, MAE, and MAPE of 2.80 t/ha, 2.58 t/ha, and 5.41%, respectively. The performance of the ANN model in the training phase was compared to multiple linear regression (MLR) models using values of R2; for MLR models for all cultivars, R2 ranged between 0.151 and 0.844, while the R2 value for the ANN was 0.87. Moreover, the ANN model gave the best performance criteria for evaluation of citrus yield prediction with a high R2, low root mean squared error, and low mean absolute error compared to the performance criteria of data mining algorithms such as K-nearest neighbor (KNN), KStar, and support vector regression. These encouraging outcomes show how the current ANN model can be used to estimate fruit yields, including citrus fruits and other types of fruit. The novelty of the proposed ANN model lies in the combination of weather parameters and accumulated heat units for accurate citrus yield prediction, specifically tailored for Egyptian regional citrus crops. Furthermore, especially in low- to middle-income countries such as Egypt, the findings of this study can greatly enhance the reliance on statistics when making decisions regarding agriculture and climate change. The citrus industry can benefit greatly from these discoveries, which can help with optimization, harvest planning, and postharvest logistics. We recommended furthering proving the robustness and generalization ability of the results in this study by adding more data points.

Soil radium, soil gas radon and indoor radon empirical relationships to assist in post-closure impact assessment related to near-surface radioactive waste disposal

This paper presents an application of the design of experiment(DoE) techniques to determine the optimized parameters of the artificial neural network (ANN)model, which are used to estimate the force from the electromyogram (sEMG) signals. The accuracy of the ANN model is highly dependent on the network parameter settings. There are plenty of algorithms that are used to obtain the optimal ANN settings. However, to the best of our knowledge, no regression analysis has yet been used to model the effect of each parameter as well as presenting the percent contribution and significance level of the ANN parameters for force estimation. In this paper, the sEMG experimental data is collected, and the ANN parameters are regulated based on an orthogonal array design table to train the ANN model. The Taguchi method helps us to find the optimal parameters settings. The analysis of variance (ANOVA) technique is then used to obtain the significance level as well as the contribution percentage of each parameter I order to optimize ANN’ modeling in the human force estimation. The results obtained indicate that DoE is a promising solution to estimate the human force from the sEMG signals.

Electrical conductivity (EC) is an important indicator for monitoring water quality in riverine systems. EC is inherently associated with the concentration of dissolved ionic compounds present in aqueous environments, including various salts and minerals. EC estimations are crucial for environmental monitoring and the overall health assessment of aquatic ecosystems. The present study investigated the application of discrete wavelet transform (DWT) in conjunction with artificial neural networks (ANNs) and multiple linear regression (MLR) models to predict daily river water EC. For this purpose, daily river discharge (Q) and EC time series from a hydrology station on the Medina River in San Antonio, Texas, USA, were used. DWT was used to decompose the daily data into several subseries. Then, to estimate one-day-ahead EC values, these subseries were introduced to the ANN and MLR models. To assess the prediction accuracy of the improved wavelet-neural network (WANN) and wavelet-regression (WR) models, EC estimation was also carried out using MLR and ANN models with the original data. Both the WANN and WR techniques outperformed single MLR and ANN methods. A comparison of the results indicated that the WR model had superior performance than the WANN, MLR, and ANN models for daily EC prediction. The R2 values for the WR, WANN, MLR, and ANN models were 0.92, 0.87, 0.74, and 0.74, respectively. For the WR model, the root-mean-square error (RMSE) was 45.55, 46.08, and 25.19% less than those presented by the MLR, ANN, and WANN models, respectively. By the application of the WR method, an accurate daily EC estimator formula was obtained as well. The WR model also satisfactorily simulated the hysteresis in EC, demonstrating the effectiveness of wavelet analysis in extracting essential information embedded in original data.

This article compares two modeling approaches for optimal operation of a turbo chiller installed in an office building: (1) a machine learning model developed with artificial neural network (ANN) and (2) a hybrid machine learning model developed with the ANN model and available physical knowledge of the chiller. Before developing the ANN model of the chiller, the authors used Gaussian mixture model in order to check the validity of measured data. Then, the hybrid model was developed by combining the ANN model and physics-based regression equations from the EnergyPlus engineering reference. It was found that both the ANN and hybrid ANN model are satisfactory to predict the chiller’s power consumption: mean bias error (MBE) = −2.63%, coefficient of variation of the root mean square error (CVRMSE) = 8.05% by the ANN model; MBE = −3.99%, CVRMSE = 11.98% by the hybrid ANN model. However, the hybrid model requires fewer inputs (four inputs) than the ANN model (eight inputs). The energy savings of both models are similar coefficient of performance (COP) = 4.32 by the optimal operation of the ANN model; COP = 4.44 by the optimal operation of the hybrid ANN model. In addition, the hybrid ANN model can be applied where the ANN model is unable to provide accurate predictions.

Comparative analysis of deep learning models for predicting biocompatibility in tissue scaffold images.

Artificial neural network modeling of meteorological and geological influences on indoor radon concentration in selected tertiary institutions in Southwestern Nigeria