Machine learning as a tool for analysing the impact of environmental parameters on the radon exhalation rate from soil

Impact of meteorological parameters on soil radon at Kolkata, India: investigation using machine learning techniques

The daily soil radon activity has been measured continuously over a year with BARASOL BMC2 probe at a measuring site of Jadavpur University Campus in Kolkata, India. The dependency of soil radon activity with different atmospheric parameters such as soil temperature, soil pressure, humidity, air temperature, and rainfall has been also analyzed. The whole study period is divided in four seasons as proposed by the Indian Meteorological Department (IMD). Minimum soil radon level has been observed during the winter season (December-February). On the other hand, higher soil radon level has been observed both for summer and monsoon. Except soil pressure, all other variables have shown positive correlation with soil radon activity. Among five variables, soil temperature has been the most significant variable in terms of correlation with soil radon level whereas maximum humidity has been the least significant correlated variable. It has been observed that considerable reduction of soil radon level occurred after four heavy rainfall events during the study period. The combined effect of these multi-parameters on soil radon gas has been evaluated using machine learning methods like principal component regression (PCR), support vector regression (SVR), random forest regression (RF), and gradient boosting machine (GBM). In terms of performances, RF and GBM have performed much better than SVR and PCR. More robust and consistent results have been obtained for GBM during both training and testing periods.

INFLUENCE OF SAMPLING FLOW RATE ON THORON EXHALATION RATE MEASUREMENTS BY THE CIRCULATION METHOD

Researchers have used various methods to obtain the exhalation rates of radon and thoron from soil and building materials. One of the typical methods for radon exhalation rate is the circulation method using an accumulation container, an external or internal sampling pump and a continuous radon monitor. However, it is necessary to consider sampling flow rate if this method is applied to exhalation rate measurement for thoron due to its short half-life. Based on a calibration experiment, the measured thoron concentrations obtained by an electrostatic collection type radon and thoron monitor (RAD7) were found to be influenced strongly by the sampling flow rate. It was also found that the thoron exhalation rate from a soil sample depended on the pressure difference which was proportional to the increasing sampling flow rate. The thoron exhalation rate measured at the generally used sampling flow rate of the internal sampling pump of the RAD7 was overestimated compared with the value at 0 L min-1.

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R² = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.

Identification of sources with abnormal radon exhalation rates based on radon concentrations in underground environments

When there is poor ventilation or an irregular radon exhalation rate in an underground environment, it is necessary to judge whether the radon concentration is abnormal. To protect personal safety and health from radon gas, it is necessary to track the location of an abnormal radon source and measure its release rate to formulate emergency control and eradication measures. However, in an underground environment, it is impossible to fully monitor the radon concentration at every location, and as a result, blind spots are present, making it difficult to obtain timely early warnings in the event of an abnormal radon exhalation rate. Based on the distribution of radon concentration in an underground environment, this research establishes a theoretical mathematical model of an underground ventilation network containing radon. We combined particle swarm optimization with the long short-term memory (PSO-LSTM) method, which uses part of a time series signal of monitored radon concentrations to track the location of an abnormal radon source and determine an abnormal radon exhalation rate. Performing experiments of theoretical examples and actual underground ventilation environment examples, we prove the necessity of optimizing the monitoring position of the angle-connected ventilation network. The results show that the PSO-LSTM model based on radon concentration monitoring can process time series signals. Its accuracy and decision coefficient greater that is than 0.9 indicate the reliability of the model and method.

Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt

To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) methodologies. Hydrogeological and hydrogeochemical data of the groundwater wells in three periods (1996, 2007, and 2018) were used as input data for the AI methods. All the studied indicators were enrolled in feature extraction process where the most significant inputs were determined, including the studied year, the distance from the shoreline, the aquifer type, and the hydraulic head. These inputs were used to build four basic AI models to get the optimal prediction results of the used indicators (the base exchange index (BEX), the groundwater quality index for seawater intrusion (GQI_SWI), and water quality). The machine learning models utilized in this study are logistic regression, Gaussian process regression, feedforward backpropagation neural networks (FFBPN), and deep learning-based long-short-term memory. The FFBPN model achieved higher evaluation results than other models in terms of root mean square error (RMSE) and R² values in the testing phase, with R² values of 0.9667, 0.9316, and 0.9259 for BEX, GQI_SWI, and water quality, respectively. Accordingly, the FFBPN was used to build a predictive model for electrical conductivity for the years 2020 and 2030. Reasonable results were attained despite the imbalanced nature of the dataset for different times and sample sizes. The results show that the 1000 μS/cm boundary is expected to move inland ~9.5 km (eastern part) to ~10 km (western part) to ~12.4 km (central part) between 2018 and 2030. This encroachment would be hazardous to water resources and agriculture unless action plans are taken.

Correction of the travel time estimation for ambulances of the red cross Tijuana using machine learning

This paper addresses the problem of estimating the response time to a medical emergency, specifically from the Red Cross of Tijuana (RCT), which provides most of the emergency medical services (EMS) in the city of Tijuana, Mexico. For institutions with low funding, such as the RCT, relying on free or open source mapping systems to estimate travel times is necessary but also error prone because these systems are not tuned for ambulance movements within a city. Therefore, this work formulates a supervised machine learning problem where the goal is to predict the difference in travel time between the ground truth travel time provided by a GPS and the approximation offered by two mapping systems, Google Maps (GM) and Open Source Routing Machine (OSRM). To this end, this work develops a new dataset based on the EMS logs of the RCT, considering calls from January 2017 to April 2017. The posed learning problem is solved under different scenarios, including using an off-the-shelf default configuration of a Random Forest classifier, applying a hyper-parameter optimization process and using an Auto Machine Learning (AutoML) system. Considering all of the dataset for GM, test accuracy was 69.6% for the first two learning approaches and 71.6% using AutoML. For OSRM, performance was 64.6%, 65.2% and 66.4% for each of the learning approaches, respectively. Results show that it is possible to predict the level by which a mapping system over or under estimates the true travel time of an ambulance. Finally, the impact of the model is demonstrated by using it to solve the ambulance location problem, with notable differences in ambulance deployments and percentage of double coverage achieved relative to using the standard mapping system. Results show that without correcting the travel time the percentage of double coverage is 83.90%; on the other hand, double coverage reaches 100% when applying travel time correction.