Jiawu Chen
Nanchang University
8 Papers
3 Citations
Jiawu Chen is an academic researcher from Nanchang University. The author has contributed to research in topics: Computer science & Landslide. The author has an hindex of 6, co-authored 6 publications.
Chat about Author
Papers
Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models
TL;DR: It can be concluded that selecting recorded landslides as prior knowledge to train and test the LSP models is the key reason for the higher prediction accuracy of the SML models, while the lack of a priori knowledge and target guidance is an important reasons for the low LSP accuracy ofThe USML models.
Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold
TL;DR: Wang et al. as mentioned in this paper used frequency ratio analysis-based logistic regression (LR), support vector machine (SVM) and random forest (RF) models to predict landslide susceptibility for machine learning model comparison.
131
Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models
TL;DR: Wang et al. as discussed by the authors explored the influences of different attribute interval numbers (AINs) in the frequency ratio (FR) analysis of continuous environmental factors and the influence of different data-based models on the uncertainties of landslide susceptibility prediction.
109
Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network
TL;DR: A deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field in cascade-parallel form was proposed for making LSPs based on remote sensing images and a geographic information system (GIS).
109
Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models
TL;DR: The results indicate that SE factor plays the most important role in landslide susceptibility prediction among all 10 predisposing factors under both 30 m and 60 m resolutions; the SE-based models have more accurate landslides susceptibility prediction than the single models with no SE factor; and the C5.0 DT and SVM models show higher landslide susceptibility Prediction performance than the MLP and LR models.
65