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 …

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 …

The cementitious composites have different properties in the changing environment. Thus,
knowing their mechanical properties is very important for safety reasons. The most important …

Across the globe, landslides have been recognized as one of the most detrimental
geological calamities, especially in hilly terrains. However, the correct determination of …

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 …

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 …

不同机器学习预测滑坡易发性的建模过程及其不确定性有所差异, 另外如何有效识别滑坡易发性
的主控因子意义重大. 针对上述问题, 以支持向量机(support vector machine, 简称SVM) …

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 …

Landslide susceptibility analysis can provide theoretical support for landslide risk
management. However, some susceptibility analyses are not sufficiently interpretable …

Landslide susceptibility assessment is crucial in predicting landslide occurrence and
potential risks. However, traditional methods usually emphasize on larger regions of …