Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting.

Adriaan L Van Natijne,Roderik C Lindenbergh,Thom A Bogaard

doi:10.3390/s20051425

Abstract

Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.

Highlights

Landslides are a major hazard to human life and society, killing over 55,000 people over the period of 2004–2016 [1], and causing an estimated average economic loss of AC 4.7 billion per year in Europe alone [2]
We focus on slow moving, deep-seated landslides on natural slopes, for which deformation is controlled by hydro-meteorological conditions
We showed that there are satellite remote sensing products available that capture the major contributors to the landslide process as well as the continuous, slow deformation of deep-seated landslides themselves

Summary

Introduction

Landslides are a major hazard to human life and society, killing over 55,000 people over the period of 2004–2016 [1], and causing an estimated average economic loss of AC 4.7 billion per year in Europe alone [2]. New data and data integration methods offer new possibilities for landslide forecasting, especially for slow-moving, deep-seated landslides. We focus on slow moving, deep-seated landslides on natural slopes, for which deformation is controlled by hydro-meteorological conditions. These landslides are characterized by gradual, non-catastrophic deformations of millimeters to decimeters per year and can be monitored and modeled over at least multiple years. They are considered to be in a state of so-called limit-equilibrium undergoing continuous deformation, but may accelerate or stabilize when conditions change [5]

Methods

Discussion

Conclusion