Artificial intelligence and machine learning have attracted significant attention in the
preparation of landslide susceptibility maps (LSMs) over the years. Achieving considerable …

In landslide susceptibility assessment (LSA), inventory incompleteness impacts the accuracy
of different models to varying degrees. However, this area remains under-researched. This …

Selecting the best planting area for blueberries is an essential issue in agriculture. To better
improve the effectiveness of blueberry cultivation, a machine learning-based classification …

The analysis and evaluation of landslide susceptibility are of great significance in preventing
and managing geological hazards. Aiming at the problems of insufficient information caused …

At first glance, assessing future landslide‐exposed population appears to be a
straightforward task if landslide hazard estimates, climate change, and population …

Landslides are a significant geological hazard that annually cause extensive damage and
loss of life worldwide. Therefore, it is crucial to have reliable prediction models for landslide …

This study attempts to explore the essential influencing factors of landslides and explores
the effects of different datasets on landslide susceptibility map** (LSM) at six grid …

Global warming and unpredictable nature possess a negative impact on fisheries and the
daily activities of other habitats. GIS and remote sensing approach is an effective tool to …

The acquisition of landslide inventory represents a pivotal challenge in landslide
susceptibility map**. Existing landslide susceptibility maps (LSMs) predominantly rely on …

Flash flood causes severe damage to the environment and human life across the world, no
exception is Bangladesh. Severe flash floods affect the northeastern portion of Bangladesh …