Machine learning in environmental research: common pitfalls and best practices
Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …
sets and decipher complex relationships between system variables. However, due to the …
Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
Landslides are one of the catastrophic natural hazards that occur in mountainous areas,
leading to loss of life, damage to properties, and economic disruption. Landslide …
leading to loss of life, damage to properties, and economic disruption. Landslide …
[HTML][HTML] Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms
The cementitious composites have different properties in the changing environment. Thus,
knowing their mechanical properties is very important for safety reasons. The most important …
knowing their mechanical properties is very important for safety reasons. The most important …
Predictive Performances of ensemble machine learning algorithms in landslide susceptibility map** using random forest, extreme gradient boosting (XGBoost) and …
Across the globe, landslides have been recognized as one of the most detrimental
geological calamities, especially in hilly terrains. However, the correct determination of …
geological calamities, especially in hilly terrains. However, the correct determination of …
[HTML][HTML] Landslide susceptibility map** using hybrid random forest with GeoDetector and RFE for factor optimization
The present study aims to develop two hybrid models to optimize the factors and enhance
the predictive ability of the landslide susceptibility models. For this, a landslide inventory …
the predictive ability of the landslide susceptibility models. For this, a landslide inventory …
[HTML][HTML] Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models
Y Wei, H Qiu, Z Liu, W Huangfu, Y Zhu, Y Liu… - Geoscience …, 2024 - Elsevier
Landslide susceptibility assessment is crucial in predicting landslide occurrence and
potential risks. However, traditional methods usually emphasize on larger regions of …
potential risks. However, traditional methods usually emphasize on larger regions of …
Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous …
Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These
landslides can be deadly to the community living downslope with their fast pace, turning …
landslides can be deadly to the community living downslope with their fast pace, turning …
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
The main objective of the current study was to introduce a Deep Learning Neural Network
(DLNN) model in landslide susceptibility assessments and compare its predictive …
(DLNN) model in landslide susceptibility assessments and compare its predictive …
Landslide Susceptibility map** using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing
W Zhang, Y He, L Wang, S Liu, X Meng - Geological Journal, 2023 - Wiley Online Library
Landslide susceptibility analysis can provide theoretical support for landslide risk
management. However, some susceptibility analyses are not sufficiently interpretable …
management. However, some susceptibility analyses are not sufficiently interpretable …
A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
With the increasing threat of recurring landslides, susceptibility maps are expected to play a
bigger role in promoting our understanding of future landslides and their magnitude. This …
bigger role in promoting our understanding of future landslides and their magnitude. This …