[HTML][HTML] An explainable AI (XAI) model for landslide susceptibility modeling
Landslides are among the most devastating natural hazards, severely impacting human
lives and damaging property and infrastructure. Landslide susceptibility maps, which help to …
lives and damaging property and infrastructure. Landslide susceptibility maps, which help to …
[HTML][HTML] GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms
Hazards and disasters have always negative impacts on the way of life. Landslide is an
overwhelming natural as well as man-made disaster that causes loss of natural resources …
overwhelming natural as well as man-made disaster that causes loss of natural resources …
A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset
As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it
is important to understand how decisions are made. Fairness, and transparency becomes …
is important to understand how decisions are made. Fairness, and transparency becomes …
Groundwater potential mapping using remote sensing and GIS-based machine learning techniques
Adequate groundwater development for the rural population is essential because
groundwater is an important source of drinking water and agricultural water. In this study …
groundwater is an important source of drinking water and agricultural water. In this study …
Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data
Predicting landslide occurrences can be difficult. However, failure to do so can be
catastrophic, causing unwanted tragedies such as property damage, community …
catastrophic, causing unwanted tragedies such as property damage, community …
Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity
Z Du, Z Wang, S Wu, F Zhang, R Liu - International Journal of …, 2020 - Taylor & Francis
Geographically weighted regression (GWR) is a classic and widely used approach to model
spatial non-stationarity. However, the approach makes no precise expressions of its …
spatial non-stationarity. However, the approach makes no precise expressions of its …
Optimized conditioning factors using machine learning techniques for groundwater potential mapping
Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential
when performing analyses for groundwater potential mapping. For this reason, in this work …
when performing analyses for groundwater potential mapping. For this reason, in this work …
Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms
Although the prediction of debris flow-prone areas represents a key step towards reducing
damages, modeling debris flow susceptibility is complicated. In addition, the role of debris …
damages, modeling debris flow susceptibility is complicated. In addition, the role of debris …
Landslide susceptibility model using artificial neural network (ANN) approach in Langat river basin, Selangor, Malaysia
Landslides are a natural hazard that can endanger human life and cause severe
environmental damage. A landslide susceptibility map is essential for planning, managing …
environmental damage. A landslide susceptibility map is essential for planning, managing …
Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction
Floods are one of the most common natural disasters in the world that affect all aspects of
life, including human beings, agriculture, industry, and education. Research for developing …
life, including human beings, agriculture, industry, and education. Research for developing …