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Prediction of PM2.5 concentration based on the weighted RF-LSTM model
Accurate prediction of PM2.5 concentrations can provide a solid foundation for preventing and controlling air pollution. When the Long Short-Term...
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Predicting Short-Term Rockburst Using RF–CRITIC and Improved Cloud Model
Rockburst is a common ground pressure disaster in underground geotechnical engineering. The frequent occurrence of rockburst hazards severely...
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Optimal flood susceptibility model based on performance comparisons of LR, EGB, and RF algorithms
Wadi El-Matulla, located in the eastern desert of Egypt, is the most important water basin. The Qift–Qusayr highway (west–east direction) and the...
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Application of random forest (RF) for flood levels prediction in Lower Ogun Basin, Nigeria
This study evaluates the performance of random forest (RF) for predicting flood levels in the Lower Ogun Basin, Southwest Nigeria. Daily flood levels...
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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters
With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary...
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Classification of Precipitation Intensities from Remote Sensing Data Based on Artificial Intelligence Using RF Multi-learning
A new strategy based on random forest (RF) classifier multi-learning is elaborated for the rainfall intensities classification from remote sensing...
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Predicting liquefaction-induced lateral spreading by using the multigene genetic programming (MGGP), multilayer perceptron (MLP), and random forest (RF) techniques
Landslides refer to a wide range of processes that result in the downward and outward movement of slope-forming materials, which may spread....
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A prediction model for blasted block size grou** based on HC and RF-GA-BP neural network
Blast block prediction is a complex non-stationary, nonlinear problem, the contribution of factors affecting results varies for different external...
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Performance of Hybrid SCA-RF and HHO-RF Models for Predicting Backbreak in Open-Pit Mine Blasting Operations
Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling...
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The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSO) to Predict the Soil Liquefaction
In this study, post-liquefaction Standard penetration test (SPT) data from the Chi-Chi earthquake was collected and included into a Random Forest...
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PCA-RF model for Dendrolimus punctatus Walker damage detection
At the present stage, the effective coupling information of “ground-space” is still a fundamental way to detect forest pest damage rapidly and...
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Application of coupling physics–based model TRIGRS with random forest in rainfall-induced landslide-susceptibility assessment
Most data-driven landslide-susceptibility assessment models heavily rely on statistical analyses based on geological and environmental similarity...
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An information quantity and machine learning integrated model for landslide susceptibility map** in Jiuzhaigou, China
Landslide susceptibility map** (LSM) with machine learning (ML) models highly depends on the number and accuracy of landslides (positive samples)...
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Hyperparameter tuning of supervised bagging ensemble machine learning model using Bayesian optimization for estimating stormwater quality
Physically based models (PBMs), including stormwater management model (SWMM), require a significant amount of in situ data and expertise to predict...
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A Gene-Random Forest Model for Meteorological Drought Prediction
The evolution of ensemble learning has recently offered a new approach to model complex systems. Inspired by the success of such methods, this paper...
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Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing
Liquefaction prediction is an important issue in the seismic design of engineering structures, and research on this topic has been continuing in...
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Random forest-based nowcast model for rainfall
In the present study, a model has been developed for nowcasting using the Automatic Weather Station (AWS) data collected from Thiruvananthapuram,...
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Development of risk maps for flood, landslide, and soil erosion using machine learning model
Natural hazards, such as flood, landslide, and erosion, are the reality of human life. spatial prediction of these hazards and their effectiveness...
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A refined zenith tropospheric delay model for Mainland China based on the global pressure and temperature 3 (GPT3) model and random forest
Zenith Tropospheric Delay (ZTD) plays a vital role in Global Navigation Satellite System (GNSS) navigation, positioning, and meteorology. The...
