Use of random forest algorithm for predictive modelling of transfer factor soil-plant for radiocaesium: A feasibility study

Urso, Laura; Petermann, Eric; Gnädinger, Friederike; Hartmann, Philipp

doi:10.1016/j.jenvrad.2023.107309

Cited by 3 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Random Forest Algorithm Many decision trees' outputs are combined by the popular Random Forest (RF) machine learning technique to produce a single result: a prediction model for various interests [20]. Random Forest uses two methods to compensate for the classification deficiencies of individual regression trees.…”

Section: 5mentioning

confidence: 99%

Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients

Sari,

Melyani,

Arrazak

et al. 2024

PREDATECS

View full text Add to dashboard Cite

Stroke is the second most common cause of death globally, making up about 11% of all deaths from health-related deaths each year, the condition varies from mild to severe, with the potential for permanent or temporary damage, caused by non-traumatic cerebral circulatory disorders. This research began with data understanding through the acquisition of a stroke patient health dataset from Kaggle, consisting of 5110 records. The pre-processing stage involved transforming the data to optimize processing, converting numeric attributes to nominal, and preparing training and test data. The focus then shifted to stroke disease classification using Random Forest, Support Vector Machines, and Neural Networks algorithms. Data processing results from the Kaggle dataset showed high performance, with Random Forest achieving 98.58% accuracy, SVM 94.11%, and Neural Network 95.72%. Although SVM has the highest recall (99.41%), while Random Forest and ANN have high but slightly lower recall rates, 98.58% and 95.72% respectively. Model selection depends on the needs of the application, either focusing on precision, recall, or a balance of both. This research contributes to further understanding of stroke diagnosis and introduces new potential for classifying the disease.

show abstract

Section: 5mentioning

confidence: 99%

Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients

Sari,

Melyani,

Arrazak

et al. 2024

PREDATECS

View full text Add to dashboard Cite

show abstract

Recent trends in the phytoremediation of radionuclide contamination of soil by cesium and strontium: Sources, mechanisms and methods: A comprehensive review

Ammar,

Nouira,

El Mouridi

et al. 2024

Chemosphere

View full text Add to dashboard Cite

Landscape Metrics as Ecological Indicators for PM10 Prediction in European Cities

Sohrab,

Csikós,

Szilassi

2024

Land

View full text Add to dashboard Cite

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death in Europe. Urban populations are particularly exposed to high concentrations of air pollutants, such as particulate matter smaller than 10 µm (PM10). Understanding the spatiotemporal variations of PM10 is essential for developing effective control strategies. This study aimed to enhance PM10 prediction models by integrating landscape metrics as ecological indicators into our previous models, assessing their significance in monthly average PM10 concentrations, and analyzing their correlations with PM10 air pollution across European urban landscapes during heating (cold) and non-heating (warm) seasons. In our previous research, we only calculated the proportion of land uses (PLANDs), but according to our current research hypothesis, landscape metrics have a significant impact on PM10 air quality. Therefore, we expanded our independent variables by incorporating landscape metrics that capture compositional heterogeneity, including the Shannon diversity index (SHDI), as well as metrics that reflect configurational heterogeneity in urban landscapes, such as the Mean Patch Area (MPA) and Shape Index (SHI). Considering data from 1216 European air quality (AQ) stations, we applied the Random Forest model using cross-validation to discover patterns and complex relationships. Climatological factors, such as monthly average temperature, wind speed, precipitation, and mean sea level air pressure, emerged as key predictors, particularly during the heating season when the impact of temperature on PM10 prediction increased from 5.80% to 22.46% at 3 km. Landscape metrics, including the SHDI, MPA, and SHI, were significantly related to the monthly average PM10 concentration. The SHDI was negatively correlated with PM10 levels, suggesting that heterogeneous landscapes could help mitigate pollution. Our enhanced model achieved an R² of 0.58 in the 1000 m buffer zone and 0.66 in the 3000 m buffer zone, underscoring the utility of these variables in improving PM10 predictions. Our findings suggest that increased urban landscape complexity, smaller patch sizes, and more fragmented land uses associated with PM10 sources such as built-up areas, along with larger and more evenly distributed green spaces, can contribute to the control and reduction of PM10 pollution.

show abstract

Use of random forest algorithm for predictive modelling of transfer factor soil-plant for radiocaesium: A feasibility study

Cited by 3 publications

References 19 publications

Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients

Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients

Recent trends in the phytoremediation of radionuclide contamination of soil by cesium and strontium: Sources, mechanisms and methods: A comprehensive review

Landscape Metrics as Ecological Indicators for PM10 Prediction in European Cities

Contact Info

Product

Resources

About