Strengthening Landslide Prediction Amid Growing Risks

Landslides pose increasingly bigger risks, especially to communities in hilly and mountainous areas. Land use change, deforestation, and climate change have made slopes less stable, raising the risk of sudden soil and rock movement. This underscores the urgency for better anticipatory mechanisms as a part of disaster risk reduction efforts. In Hong Kong, researchers found a computer model with higher accuracy to support better landslide prediction.

A Growing Threat

Landslides are dangerous natural disasters that involve the collapsing of soil, rocks, or debris down a slope. This disaster can occur slowly or suddenly, destroying everything in its path.

In the past, landslides were less common because natural vegetation was mostly untouched, and human activities were limited. Today, landslides threats have worsened, especially for people living in hilly or mountainous areas.

Among the main causes of landslides are land use change and deforestation. When vegetation is removed from higher ground, the soil loses its ability to absorb water properly. The absence of strong roots to hold the soil together leads to increased chances of soil movement when pressure from rainwater occurs, especially on steep slopes. Furthermore, climate change also plays a role, as it can lead to more frequent and intense rainfall, which further increases the risk of landslides.

In 2024, there were 708 recorded fatal landslides globally, which was exceptionally higher than the previous years. These events have taken hundreds of lives, left many more injured, and caused serious damage to infrastructure and daily mobility in affected areas. As landslides become more frequent and severe, the need for effective landslide prediction, early warning systems, and disaster preparedness facilities grows more urgent.

New Method for Landslide Prediction and More

Researchers at the Hong Kong University of Science and Technology (HKUST) have developed a computer model to better understand how materials like soil, sand, and powder move. The model is called Pore Unit Assembly-Discrete Element Model (PUA-DEM), and it helps simulate how these particles behave when mixed with water and air which are the conditions often found in real-life settings like landslides or irrigation systems.

This model is important because it solves a tricky problem that older models couldn’t handle well, which is how partially wet soil behaves. In those conditions, forces like water tension, stickiness, and shifting pressure can be hard to predict. But PUA-DEM uses advanced physics to simulate how water, air, and particles react with each other in real time.

Therefore, the model can predict events like landslides or soil collapse with better accuracy, making it a valuable tool for early warning systems. Led by Professor Zhao Jidong, the research team believe this model could be used for beyond landslide prediction. It can help with farming by improving irrigation systems, support cleaner energy extraction, and even make pharmaceutical production more efficient and reliable.

Protecting Communities Through Innovation and Awareness

As the Earth grows hotter and the weather gets more unpredictable, we are at risk of experiencing more severe natural disasters. Therefore, strengthening disaster management systems, including landslide prediction, must combine new scientific tools with local knowledge and strategies. Governments, scientists, and communities need to work together to develop early warning systems, manage the land better, and raise awareness about landslide risks. These efforts can help reduce harm and save lives.

At the same time, protecting forests and natural vegetation plays a big role in preventing landslides by holding the soil together and keeping slopes strong. Ultimately, we must not forget to advance the efforts to care for our environment to protect people and create a safer place for all.

Editor: Nazalea Kusuma & Kresentia Madina